Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis

Leon Di Stefano; Elizabeth L Ogburn; Malathi Ram; Daniel O Scharfstein; Tianjing Li; Preeti Khanal; Sheriza N Baksh; Nichol McBee; Joshua Gruber; Marianne R Gildea; Megan R Clark; Neil A Goldenberg; Yussef Bennani; Samuel M Brown; Whitney R Buckel; Meredith E Clement; Mark J Mulligan; Jane A O’Halloran; Adriana M Rauseo; Wesley H Self; Matthew W Semler; Todd Seto; Jason E Stout; Robert J Ulrich; Jennifer Victory; Barbara E Bierer; Daniel F Hanley; Daniel Freilich; on behalf of the Pandemic Response COVID-19 Research Collaboration Platform for HCQ/CQ Pooled Analyses

doi:10.1371/journal.pone.0273526

. 2022 Sep 29;17(9):e0273526. doi: 10.1371/journal.pone.0273526

Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis

Leon Di Stefano ¹, Elizabeth L Ogburn ¹, Malathi Ram ^2,³, Daniel O Scharfstein ⁴, Tianjing Li ⁵, Preeti Khanal ³, Sheriza N Baksh ⁶, Nichol McBee ³, Joshua Gruber ³, Marianne R Gildea ^3,^¤, Megan R Clark ³, Neil A Goldenberg ^7,^8,⁹, Yussef Bennani ^10,¹¹, Samuel M Brown ^12,¹³, Whitney R Buckel ¹⁴, Meredith E Clement ^10,¹¹, Mark J Mulligan ^15,¹⁶, Jane A O’Halloran ¹⁷, Adriana M Rauseo ¹⁷, Wesley H Self ¹⁸, Matthew W Semler ¹⁹, Todd Seto ²⁰, Jason E Stout ²¹, Robert J Ulrich ¹⁵, Jennifer Victory ²², Barbara E Bierer ^23,²⁴, Daniel F Hanley ³, Daniel Freilich ^25,^*; on behalf of the Pandemic Response COVID-19 Research Collaboration Platform for HCQ/CQ Pooled Analyses^¶

Editor: A M Abd El-Aty²⁶

¹Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

²Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

³Division of Brain Injury Outcomes, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America

⁴Division of Biostatistics, Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah, United States of America

⁵University of Colorado Denver, Anschutz Medical Campus, Denver, Colorado, United States of America

⁶Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

⁷Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America

⁸Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America

⁹Johns Hopkins All Children’s Institute for Clinical and Translational Research, Johns Hopkins All Children’s Hospital, St. Petersburg, Florida, United States of America

¹⁰Louisiana State University Health Sciences Center, New Orleans, Louisiana, United States of America

¹¹University Medical Center, New Orleans, New Orleans, Louisiana, United States of America

¹²Division of Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, Utah, United States of America

¹³University of Utah, Salt Lake City, Utah, United States of America

¹⁴Pharmacy Services, Intermountain Healthcare, Murray, Utah, United States of America

¹⁵Department of Medicine, Division of Infectious Diseases and Immunology, New York University Grossman School of Medicine, New York, New York, United States of America

¹⁶Vaccine Center, New York University Grossman School of Medicine, New York, New York, United States of America

¹⁷Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, United States of America

¹⁸Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America

¹⁹Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America

²⁰Department of Medicine, University of Hawaii John A. Burns School of Medicine, Honolulu, Hawaii, United States of America

²¹Division of Infectious Diseases and International Health, Duke University Medical Center, Durham, North Carolina, United States of America

²²Bassett Research Institute, Bassett Medical Center, Cooperstown, New York, United States of America

²³Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America

²⁴Harvard Medical School, Boston, Massachusetts, United States of America

²⁵Department of Internal Medicine, Division of Infectious Diseases, Bassett Medical Center, Cooperstown, New York, United States of America

²⁶Cairo University, EGYPT

Competing Interests: The authors have read the journal’s policy and have the following competing interests: S.N.B., N.M., M.R.C., and D.F.H. report receiving research funding from the Department of Defense for clinical trials of convalescent plasma for COVID-19 outside the submitted work. N.A.G. reports receiving salary support from the National Institutes of Health (NIH) National Center for Advancing Translational Sciences via a Johns Hopkins Clinical and Translational Science Award outside the submitted work. Y.B. reports being a site investigator for Janssen outside the submitted work. S.M.B. reports service as chair of a data and safety monitoring board for a Hamilton clinical trial in respiratory failure; fees paid to Intermountain Healthcare from Faron Pharmaceuticals and Sedana Pharmaceuticals for steering committee service for a clinical trial in acute respiratory distress syndrome; research grants to Intermountain Healthcare from Janssen, NIH, Centers for Disease Control and Prevention, and Department of Defense; and royalties from Oxford University Press and Brigham Young University, outside the submitted work. M.E.C. reports service on a Roche advisory board and as a site investigator for Janssen outside the submitted work. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

^¤

Current address: FHI 360, Durham, North Carolina, United States of America

¶ Membership of the Pandemic Response COVID-19 Research Collaboration Platform for HCQ/CQ Pooled Analyses is provided in the Acknowledgments.

^✉

* E-mail: daniel.freilich@bassett.org

Roles

Leon Di Stefano: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Elizabeth L Ogburn: Conceptualization, Formal analysis, Resources, Supervision, Writing – review & editing

Malathi Ram: Data curation, Project administration, Writing – original draft, Writing – review & editing

Daniel O Scharfstein: Conceptualization, Formal analysis, Methodology, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

Tianjing Li: Conceptualization, Formal analysis, Resources, Writing – original draft, Writing – review & editing

Preeti Khanal: Project administration, Writing – original draft, Writing – review & editing

Sheriza N Baksh: Writing – original draft, Writing – review & editing

Nichol McBee: Project administration, Writing – review & editing

Joshua Gruber: Writing – review & editing

Marianne R Gildea: Project administration, Writing – review & editing

Megan R Clark: Visualization, Writing – review & editing

Neil A Goldenberg: Writing – review & editing

Yussef Bennani: Investigation, Writing – review & editing

Samuel M Brown: Investigation, Writing – review & editing

Whitney R Buckel: Investigation, Writing – review & editing

Meredith E Clement: Investigation, Writing – review & editing

Mark J Mulligan: Investigation, Writing – review & editing

Jane A O’Halloran: Investigation, Writing – review & editing

Adriana M Rauseo: Investigation, Writing – review & editing

Wesley H Self: Investigation, Writing – review & editing

Matthew W Semler: Investigation, Writing – review & editing

Todd Seto: Investigation, Writing – review & editing

Jason E Stout: Investigation, Writing – review & editing

Robert J Ulrich: Investigation, Writing – review & editing

Jennifer Victory: Investigation, Writing – review & editing

Barbara E Bierer: Conceptualization, Resources, Supervision, Writing – review & editing

Daniel F Hanley: Conceptualization, Funding acquisition, Resources, Supervision, Writing – review & editing

Daniel Freilich: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

A M Abd El-Aty: Editor

PMCID: PMC9521809 PMID: 36173983

Abstract

Background

Results from observational studies and randomized clinical trials (RCTs) have led to the consensus that hydroxychloroquine (HCQ) and chloroquine (CQ) are not effective for COVID-19 prevention or treatment. Pooling individual participant data, including unanalyzed data from trials terminated early, enables more detailed investigation of the efficacy and safety of HCQ/CQ among subgroups of hospitalized patients.

Methods

We searched ClinicalTrials.gov in May and June 2020 for US-based RCTs evaluating HCQ/CQ in hospitalized COVID-19 patients in which the outcomes defined in this study were recorded or could be extrapolated. The primary outcome was a 7-point ordinal scale measured between day 28 and 35 post enrollment; comparisons used proportional odds ratios. Harmonized de-identified data were collected via a common template spreadsheet sent to each principal investigator. The data were analyzed by fitting a prespecified Bayesian ordinal regression model and standardizing the resulting predictions.

Results

Eight of 19 trials met eligibility criteria and agreed to participate. Patient-level data were available from 770 participants (412 HCQ/CQ vs 358 control). Baseline characteristics were similar between groups. We did not find evidence of a difference in COVID-19 ordinal scores between days 28 and 35 post-enrollment in the pooled patient population (odds ratio, 0.97; 95% credible interval, 0.76–1.24; higher favors HCQ/CQ), and found no convincing evidence of meaningful treatment effect heterogeneity among prespecified subgroups. Adverse event and serious adverse event rates were numerically higher with HCQ/CQ vs control (0.39 vs 0.29 and 0.13 vs 0.09 per patient, respectively).

Conclusions

The findings of this individual participant data meta-analysis reinforce those of individual RCTs that HCQ/CQ is not efficacious for treatment of COVID-19 in hospitalized patients.

Introduction

During the COVID-19 pandemic Delta and Omicron variant surges, US daily deaths again reached 1,000–2,000, reinforcing the need for effective therapeutics. Early in the pandemic, hydroxychloroquine (HCQ) and chloroquine (CQ) received a Food and Drug Administration (FDA) Emergency Use Authorization (EUA) for treatment of hospitalized COVID-19 patients, and the drugs were administered empirically and recommended in some guidelines [1]. Supportive efficacy data relied on inconsistent results from preclinical studies [2] and small uncontrolled trials [3]. Based in part on these early data, an estimated 42% of hospitalized COVID-19 patients in the US received HCQ in March 2020 [4].

Subsequently, most retrospective-observational studies of HCQ/CQ in hospitalized COVID-19 patients found no evidence of benefit, and possibly higher mortality, with concerns about toxicities (e.g., QTc prolongation) [5–15]. Results from at least 5 randomized clinical trials (RCTs) became available in spring/summer 2020; all showed no evidence of benefit, and most showed adverse safety signals [14, 16–19]. In light of these results, most principal investigators discontinued enrollment in HCQ/CQ arms of their trials. Consequently, adequate power to reach robust conclusions regarding efficacy and safety of HCQ/CQ was no longer attainable for many incomplete trials; moreover, effect estimates in published trials were accompanied by wide confidence intervals. Nevertheless, at least 11 additional RCTs published later in the pandemic found similar results [20–30].

The goals of this study were to ensure utilization of data from unpublished RCTs evaluating HCQ/CQ by combining them with published data and to synthesize evidence on HCQ/CQ efficacy and safety in hospitalized COVID-19 patients, overall and in subpopulations of interest, by conducting an individual participant data (IPD) meta-analysis. This study design involves pooling subject-level data from multiple studies and possesses many advantages over both individual randomized trials and traditional aggregate-data meta-analyses. Individual trials are usually designed to detect overall effects; the increased sample size in an IPD meta-analysis can enable more precise estimation of subgroup effects [31]. A more diverse sample in a pooled analysis can also improve external validity over individual trials [32]. Compared with aggregate data meta-analyses, IPD meta-analyses are less vulnerable to the ecological fallacy, allow for consistent analytic choices within each study, and enable researchers to consider subgroup effects that were not considered in the original studies [33].

Methods

This project was approved by the Johns Hopkins Medicine institutional review board, with individual studies approved by their local ethics boards as deemed necessary.

Trials selection summary

The Trial Innovation Network, a National Center for Advancing Translational Sciences initiative to increase efficiency and innovation in clinical research, partnered with the COVID-19 Collaboration Platform to promote coordination among research groups running similar trials. The team contacted principal investigators of COVID-19 RCTs registered on ClinicalTrials.gov starting on April 30, 2020, and encouraged uploading study protocols to the COVID-19 Collaboration Platform (CovidCP) repository (http://covidcp.org). The platform, initiated with the goal of sharing protocols to facilitate collaboration, aims to combine data or aggregate evidence from similar studies to increase efficiency and precision.

One respondent (Bassett) had independently initiated a collaboration registry effort and performed systematic searches of ClinicalTrials.gov on May 9, 2020, and May 21, 2020, using search terms “COVID-19” and “hydroxychloroquine OR chloroquine,” and study status of “recruiting.”

Trials from the Bassett search and CovidCP repository were aggregated. Additional outreach by investigators occurred in June 2020 to studies located at Clinical and Translational Science Awards Program institutions (S3 Appendix). This combined list was the primary driver for study selection, with augmentation and refinement by additional systematic ClinicalTrials.gov searches through June 2, 2020.

US-based RCTs of HCQ/CQ to treat patients with SARS-CoV-2 infection were eligible for inclusion if patient informed consent and/or individual study institutional review board approval allowed data sharing; study institutions signed a data use agreement for the present study; the outcomes as defined in this study were recorded or could be extrapolated; and trialists agreed to participate. We excluded trials in non-hospitalized patients, trials not registered on ClinicalTrials.gov, trials without enrollment, and international trials to avoid data sharing regulatory delays. We decided to focus on inpatient studies. No individual-level exclusion criteria were imposed beyond those employed by each study.

Data collection and harmonization

A common data harmonization tool, including a data dictionary with definitions and encodings of variables, example data, and deidentification functions for dates and ages consistent with Health Insurance Portability and Accountability Act requirements, was used by 7 trial teams to create data sets that were uploaded to the data repository Vivli (https://www.vivli.org) and then downloaded by the CovidCP team. ORCHID trial data were downloaded from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) and manually harmonized by the CovidCP team. Queries about missing, unusual, or inconsistent data were resolved via direct contact with studies’ principal investigators and, in some cases, manual chart review.

Outcomes

The primary outcome was clinical improvement measured on a 7-point ordinal scale with levels (1) death; (2) hospitalized, on mechanical ventilation or extracorporeal membrane oxygenation (ECMO); (3) hospitalized, on non-invasive ventilation (BiPAP/CPAP and/or high-flow oxygen); (4) hospitalized, requiring oxygen; (5) hospitalized, not requiring oxygen; (6) not hospitalized, with limitation; and (7) not hospitalized, without limitations. This scale is relatively coarse compared to others in use (for example, the 11-point World Health Organization [WHO] scale [34]), and was chosen to make the data easier to harmonize. We prespecified an outcome window of day 28–30 post-enrollment, which was broadened to day 28–35 after data collection due to missingness (see Results). Differences in the primary outcome were assessed using proportional odds ratios.

Secondary outcomes included hospital length of stay, need for mechanical ventilation, and 28-35-day mortality. Safety outcomes included rates of overall adverse events (AEs) and serious adverse events (SAEs), and rates of specific AEs and SAEs of interest: elevated liver function tests (LFTs), QTc prolongation, and arrhythmias. Due to practical constraints, we did not attempt to synchronize adverse event definitions across the included studies.

Baseline and post-baseline variables

From each trial, baseline variables included treatment assignment, age (5-year interval bins), sex, race and ethnicity, body mass index (BMI), symptom duration, mechanical ventilation status, ordinal score, and comorbidities (cerebrovascular disease, myocardial infarction, congestive heart failure, dementia, chronic obstructive pulmonary disease, asthma, hypertension, tumor, liver disease, diabetes, smoking, and vaping), as well as post-baseline (enrollment through day 28) azithromycin and corticosteroid use (S1 Table).

Statistical analysis

The primary outcome was analyzed in two ways. First, we fit a proportional odds model with treatment indicator as the sole covariate using the “polr” command in R (version 4.0.4). Second, we fit a Bayesian proportional odds regression model including a main effect for treatment; fixed effects for sex and baseline ordinal scale (disease severity); splines of age, BMI, and a number of baseline comorbidities; and random effects for baseline ordinal scale and study. The fixed and random effects were also interacted with treatment. All fixed regression coefficients were given uniform priors. Random effects were modeled as independent, with standard deviations given independent half-t priors with 3 degrees of freedom and scale 10. The model was fit using the R package “brms” (version 2.15). Missing baseline covariates were imputed using multiple imputation by chained equations, as implemented in the R package “mice” (version 3.12); missing outcomes were treated as missing at random conditional on the included covariates. Inferences were based on fitting the model separately to each imputed data set, then pooling posterior draws across the imputations. The model was used to obtain standardized estimates of the overall treatment effect, where standardization was with respect to the empirical distribution of the baseline covariates in the pooled study population. Relative to the first approach, we employed the second approach to leverage covariates to produce more stable and accurate inferences, particularly in small subgroups.

The following subgroup analyses were prespecified: study; sex; age (≤29, 30–49, 50–69, 70–79, 80+ years); disease severity as measured by baseline ordinal score (2, 3, 4, 5); and BMI (≤20, 20–25, 25–30, 30–35, >35). Prespecified subgroup analyses based on Charlson score were replaced with a simple baseline comorbidities count (0, 1, 2, 3, ≥4) due to systematic missingness in component variables. Subgroup analyses were conducted using the two approaches discussed above. For the first, a proportional odds model was fit separately within each subgroup. For the second, the Bayesian regression model above was used to obtain standardized subgroup estimates, where standardization was with respect to the empirical distribution of covariates within subgroups. We conducted two post-hoc subgroup analyses. The first used quintiles of a baseline risk score given by the expected linear predictor for each study participant under the control condition, as per recommendations from Kent et al. [35]. The second was based on the time between symptom onset and enrollment (0–4 days, 5–7 days, ≥8 days; groups based on approximate tertiles).

All-cause 28-35-day mortality was analyzed using the same approaches. Other secondary and safety outcomes were analyzed descriptively.

To assess the sensitivity of our conclusions to the choice of model and outcome window, we (1) repeated our analysis with weakly informative priors; (2) fit an expanded model including terms for assignment to an azithromycin arm and days between symptom onset and enrollment; (3) fit a version of the main model with no treatment interactions; (4) expanded and contracted outcome windows to 28–40 and 28–30 days, respectively; and (5) re-ran our analysis with a model fit only to ORCHID’s data set, the largest of the 8 pooled trials. Sensitivity analyses 1–2 were prespecified; 3–5 were post hoc.

We also examined conditional interaction estimates in the Bayesian regression model, focusing on effects for individuals with covariates set at reference values (age 60, BMI 25, no baseline comorbidities, baseline ordinal score 5, and sex predictors set between male and female values).

Risk of bias assessment

Two investigators (T.L. and S.N.B.) assessed risk of bias associated with the effect of assignment to treatment on the primary outcome using Cochrane’s Risk of Bias 2 tool [36], with disagreements resolved through discussion (S2 Table). This assessment was not used in the data synthesis.

Registration

This study, including its statistical analysis plan (SAP), was registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD42021254261) [37] prior to receiving patient data and amended prior to analyzing outcome data. The most significant amendment was broadening the primary outcome definition from days 28–30 to 28–35 post enrollment to minimize missingness. Post-hoc changes to the analysis are shown in S3 Table. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) extension for IPD analyses (PRISMA-IPD) [38].

Results

Study characteristics

Of 19 RCTs identified in our searches (18 from ClinicalTrials.gov; 1 from personal communication that was excluded due to lack of registration on ClinicalTrials.gov), 8 met final criteria for inclusion in our analysis (Fig 1): Outcomes Related to COVID-19 Treated With Hydroxychloroquine Among In-patients With Symptomatic Disease (ORCHID; NCT04332991) [14]; Treating COVID-19 With Hydroxychloroquine (TEACH; NCT04369742) [22]; Hydroxychloroquine vs. Azithromycin for Hospitalized Patients With Suspected or Confirmed COVID-19 (HAHPS; NCT04329832) [23, 39]; Washington University 352: Open-label, Randomized Controlled Trial of Hydroxychloroquine Alone or Hydroxychloroquine Plus Azithromycin or Chloroquine Alone or Chloroquine Plus Azithromycin in the Treatment of SARS CoV-2 Infection (WU352; NCT04341727); NCT04344444; A Randomized, Controlled Clinical Trial of the Safety and Efficacy of Hydroxychloroquine for the Treatment of COVID-19 in Hospitalized Patients (OAHU-COVID19; NCT04345692); NCT04335552; and Comparison Of Therapeutics for Hospitalized Patients Infected With SARS-CoV-2 In a Pragmatic aDaptive randoMizED Clinical Trial During the COVID-19 Pandemic (COVID MED; NCT04328012) (S4–S6 Tables) [40].

Fig 1 — ^aTwo of the trials did not have study acronyms (only trial registration numbers). Abbreviations: COVID MED, Comparison Of Therapeutics for Hospitalized Patients Infected With SARS-CoV-2 In a Pragmatic aDaptive randoMizED Clinical Trial During the COVID-19 Pandemic; HAHPS, Hydroxychloroquine vs. Azithromycin for Hospitalized Patients With Suspected or Confirmed COVID-19; OAHU-COVID19, A Randomized, Controlled Clinical Trial of the Safety and Efficacy of Hydroxychloroquine for the Treatment of COVID-19 in Hospitalized Patients; ORCHID, Outcomes Related to COVID-19 Treated With Hydroxychloroquine Among In-patients With Symptomatic Disease; TEACH, Treating COVID-19 With Hydroxychloroquine; WU352, Washington University 352: Open-label, Randomized Controlled Trial of Hydroxychloroquine Alone or Hydroxychloroquine Plus Azithromycin or Chloroquine Alone or Chloroquine Plus Azithromycin in the Treatment of SARS CoV-2 Infection.

HCQ was a treatment arm in all studies; CQ was an additional treatment arm in one study (WU352). Comparators were placebo (3 trials), azithromycin (2 trials), and standard/usual care (2 trials); WU352 compared HCQ and CQ with and without azithromycin. HCQ dosing was usually (7 studies) 400 mg orally twice daily on day 1 and 200 mg twice daily on days 2–5, totaling 2,400 mg. Three trials were blinded; 5 were open-label.

Data on the prespecified primary outcome (the 7-point ordinal scale measured between days 28 and 30) was available for 90% of patients (695 out of 770). In the TEACH study, however, data was available for only 45% of patients (58 out of 128). Because of this, the decision was made to broaden the primary outcome window to days 28–35. This decision was made without examining the outcome data themselves. With the broader definition, primary outcome data was available for 76% of patients in TEACH (97 out of 128) and 95% of patients overall (734 out of 770).

Risk of bias assessment

Risk of bias judgments are summarized for the primary outcome measurement in S2 Table. Overall, ORCHID and COVID MED were rated “low risk”; the other trials were rated “some concerns.”

Patient characteristics

Among 770 patients with laboratory-confirmed SARS-CoV-2 infection, 412 were randomized to HCQ/CQ treatment (398 HCQ; 14 CQ) and 358 to the control group (Table 1). Enrollment was at a median of 6 days (IQR, 3–8 days) after symptom onset. Most patients initiated dosing on the enrollment day.

Table 1. Participant characteristics overall and in each trial.

	Overall (n = 770)		ORCHID (n = 479)		TEACH (n = 128)		HAHPS (n = 85)		WU352 (n = 30)	NCT04344444 (n = 20)		OAHU-COVID19 (n = 16)		NCT04335552 (n = 11)		COVID MED (n = 1)
	HCQ/CQ (n = 412)	Control (n = 358)	HCQ/CQ (n = 242)	Control (n = 237)	HCQ/CQ (n = 67)	Control (n = 61)	HCQ/CQ (n = 42)	Control (n = 43)	HCQ/CQ (n = 30)	HCQ/CQ (n = 15)	Control (n = 5)	HCQ/CQ (n = 10)	Control (n = 6)	HCQ/CQ (n = 6)	Control (n = 5)	Control (n = 1)
Sex, No. (%)
Female	167 (41)	158 (44)	106 (44)	105 (44)	22 (33)	30 (49)	14 (33)	19 (44)	18 (60)	2 (13)	2 (40)	4 (40)	0 (0)	1 (17)	2 (40)	0 (0)
Male	244 (59)	200 (56)	135 (56)	132 (56)	45 (67)	31 (51)	28 (67)	24 (56)	12 (40)	13 (87)	3 (60)	6 (60)	6 (100)	5 (83)	3 (60)	1 (100)
Missing/unknown	1 (0)	0 (0)	1 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Race, No. (%)
Black	95 (23)	71 (20)	58 (24)	57 (24)	14 (21)	11 (18)	0 (0)	1 (2)	18 (60)	5 (33)	2 (40)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
White	197 (48)	158 (44)	109 (45)	103 (43)	32 (48)	23 (38)	29 (69)	26 (60)	11 (37)	10 (67)	2 (40)	2 (20)	0 (0)	4 (67)	3 (60)	1 (100)
Multiple	3 (1)	3 (1)	2 (1)	3 (1)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	1 (17)	0 (0)	0 (0)
Other^a	107 (26)	113 (32)	73 (30)	74 (31)	14 (21)	18 (30)	10 (24)	13 (30)	1 (3)	0 (0)	0 (0)	8 (80)	6 (100)	1 (17)	2 (40)	0 (0)
Unavailable	10 (2)	13 (4)	0 (0)	0 (0)	7 (10)	9 (15)	3 (7)	3 (7)	0 (0)	0 (0)	1 (20)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Ethnicity, No. (%)
Hispanic	144 (35)	135 (38)	91 (38)	87 (37)	25 (37)	25 (41)	15 (36)	17 (40)	1 (3)	9 (60)	3 (60)	1 (10)	0 (0)	2 (33)	3 (60)	0 (0)
Not Hispanic	248 (60)	210 (59)	145 (60)	143 (60)	42 (63)	36 (59)	27 (64)	24 (56)	25 (83)	0 (0)	0 (0)	9 (90)	6 (100)	0 (0)	0 (0)	1 (100)
Unavailable	20 (5)	13 (4)	6 (2)	7 (3)	0 (0)	0 (0)	0 (0)	2 (5)	4 (13)	6 (40)	2 (40)	0 (0)	0 (0)	4 (67)	2 (40)	0 (0)
Age (5y bins), median (IQR)	55.0 (45.0–70.0)	55.0 (45.0–65.0)	55.0 (45.0–65.0)	55.0 (40.0–65.0)	65.0 (55.0–75.0)	65.0 (55.0–75.0)	55.0 (40.0–65.0)	50.0 (40.0–60.0)	55.0 (45.0–60.0)	70.0 (62.5–75.0)	65.0 (60.0–65.0)	67.5 (56.3–70.0)	45.0 (41.3–56.3)	47.5 (41.3–61.3)	55.0 (50.0–60.0)	55.0 (55.0–55.0)
BMI
Median (IQR)	30.0 (25.7–36.1)	31.4 (27.0–37.2)	31.3 (26.4–37.2)	31.1 (27.2–36.5)	25.9 (22.9–30.5)	29.3 (24.9–35.9)	31.7 (26.6–37.4)	36.3 (30.7–41.0)	30.5 (28.0–34.0)	27.7 (24.3–34.1)	29.3 (25.4–34.2)	28.8 (26.7–34.2)	26.0 (24.0–30.5)	33.6 (30.2–36.4)	44.2 (34.5–47.9)	37.9 (37.9–37.9)
Missing, No. (%)	16 (4)	19 (5)	16 (7)	18 (8)	0 (0)	0 (0)	0 (0)	1 (2)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Baseline ordinal scale, No. (%)
2: hosp, mech vent	23 (6)	25 (7)	13 (5)	19 (8)	0 (0)	0 (0)	7 (17)	6 (14)	0 (0)	0 (0)	0 (0)	3 (30)	0 (0)	0 (0)	0 (0)	0 (0)
3: hosp, NIV	49 (12)	42 (12)	28 (12)	27 (11)	13 (19)	7 (11)	6 (14)	7 (16)	1 (3)	1 (7)	1 (20)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
4: hosp, supp ox	191 (46)	175 (49)	116 (48)	108 (46)	25 (37)	34 (56)	23 (55)	24 (56)	13 (43)	7 (47)	3 (60)	6 (60)	5 (83)	1 (17)	0 (0)	1 (100)
5: hosp, no ox	146 (35)	112 (31)	85 (35)	83 (35)	26 (39)	17 (28)	6 (14)	6 (14)	16 (53)	7 (47)	0 (0)	1 (10)	1 (17)	5 (83)	5 (100)	0 (0)
Missing	3 (1)	4 (1)	0 (0)	0 (0)	3 (4)	3 (5)	0 (0)	0 (0)	0 (0)	0 (0)	1 (20)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Baseline ordinal scale (numeric)
Mean (SD)	4.1 (0.8)	4.1 (0.8)	4.1 (0.8)	4.1 (0.9)	4.2 (0.8)	4.2 (0.6)	3.7 (0.9)	3.7(0.9)	4.5 (0.6)	4.4 (0.6)	3.8 (0.5)	3.5 (1.1)	4.2 (0.4)	4.8 (0.4)	5.0 (0.0)	4.0 (NA)
Missing, No. (%)	3 (1)	4 (1)	0 (0)	0 (0)	3 (4)	3 (5)	0 (0)	0 (0)	0 (0)	0 (0)	1 (20)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Days between symptom onset and enrollment
Median (IQR)	5.0 (3.0–8.0)	6.0 (3.0–8.0)	5.0 (3.0–7.0)	5.0 (3.0–7.0)	7.0 (3.0–9.0)	7.0 (4.0–14.0)	8.0 (5.3–12.0)	9.0 (7.0–11.0)	5.0 (3.3–9.8)	2.0 (2.0–5.5)	6.0 (2.0–10.0)	4.5 (4.0–6.5)	2.5 (0.5–3.8)	NA	NA	7.0 (7.0–7.0)
Missing, No. (%)	6 (1)	5 (1)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	6 (100)	5 (100)	0 (0)
Baseline comorbidity count
Median (IQR)	3.0 (2.0–4.0)	3.0 (2.0–4.0)	3.0 (2.0–4.0)	3.0 (2.0–4.0)	3.0 (3.0–4.0)	4.0 (3.0–5.0)	2.0 (1.0–2.0)	2.0 (1.0–3.0)	3.0 (2.0–4.0)	5.0 (4.0–5.5)	3.0 (3.0–4.0)	2.0 (2.0–3.0)	2.0 (1.3–4.3)	NA	NA	NA
Missing, No. (%)	16 (4)	15 (4)	3 (1)	2 (1)	3 (4)	2 (3)	4 (10)	5 (12)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	6 (100)	5 (100)	1 (100)
Azithromycin use (at or before d28), No. (%)
Not assigned, did not take	311 (75)	248 (69)	195 (81)	193 (81)	54 (81)	44 (72)	28 (67)	0 (0)	16 (53)	4 (27)	5 (100)	10 (100)	4 (67)	4 (67)	2 (40)	0 (0)
Not assigned, took	75 (18)	63 (18)	47 (19)	44 (19)	13 (19)	17 (28)	14 (33)	0 (0)	0 (0)	1 (7)	0 (0)	0 (0)	2 (33)	0 (0)	0 (0)	0 (0)
Assigned, took	26 (6)	45 (13)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	42 (98)	14 (47)	10 (67)	0 (0)	0 (0)	0 (0)	2 (33)	3 (60)	0 (0)
Assigned, did not take	0 (0)	1 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	1 (2)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Missing	0 (0)	1 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	1 (100)
Concurrent corticosteroid use (at or before d28), No. (%)
Yes	57 (14)	61 (17)	39 (16)	49 (21)	7 (10)	6 (10)	7 (17)	6 (14)	2 (7)	2 (13)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Missing	6 (1)	5 (1)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	6 (100)	5 (100)	0 (0)
First dose received on day of enrollment, No. (%)
Yes	386 (94)	324 (91)	241 (100)	225 (95)	50 (75)	51 (84)	41 (98)	31 (72)	29 (97)	13 (87)	5 (100)	6 (60)	6 (100)	6 (100)	5 (100)	1 (100)
Missing	6 (1)	12 (3)	0 (0)	9 (4)	4 (6)	2 (3)	1 (2)	1 (2)	1 (3)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)

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Abbreviations: BMI, body mass index; HCQ/CQ, hydroxychloroquine or chloroquine; IQR, interquartile range; NIV, noninvasive ventilation (includes BiPAP/CPAP and/or high-flow oxygen).

^aIncludes American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, and other. To protect participant privacy, ORCHID’s data set grouped three of its race variables with low frequencies (American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander). For the sake of uniformity, we combined these groups and the “Other” category for the other studies as well.

Key baseline demographics were reasonably balanced between HCQ/CQ and control populations: mean age was 57 vs 55 years, male sex 59% vs 56%, White race 48% vs 44%, mean BMI 31.6 vs 33.2, mean comorbidities 3.16 vs 3.05 per patient, and mean ordinal score 4.1 vs 4.1, respectively. Post-baseline use of corticosteroids was 14% vs 17% and of azithromycin 24.5% vs 30.2%, respectively. Six patients with BMI values <10 or >70 were deemed probable recording errors and treated as missing in primary and mortality analyses.

Primary outcome: Pooled and subgroup analysis

The standardized proportional odds ratio (OR) for ordinal score at 28–35 days was 0.97 (95% credible interval [CrI], 0.76–1.24); the corresponding unadjusted proportional OR was 0.98 (95% CI, 0.75–1.28) (Fig 2 and Table 2). These results are consistent with no effect of HCQ/CQ. We found no appreciable heterogeneity in estimated treatment-study interactions among the 8 studies after adjusting for individual-level baseline covariates (τ = 0.87 on the log odds scale; 95% CrI, 0.01–5.17); tests for publication bias were inconclusive (S1 Fig). There were no substantial effects of HCQ/CQ observed in any prespecified subgroup, nor in a post-hoc subgroup analysis based on the time between symptom onset and enrollment (Fig 2; S5 Fig). We investigated potential trends across strata of baseline ordinal score and BMI (Fig 3). We examined corresponding conditional effect estimates and found insufficient evidence to conclude that an effect of HCQ/CQ on the primary outcome differs by BMI or baseline ordinal score, after adjusting for other baseline covariates. These and other conditional effect analyses are shown in S2 Fig.

Fig 2 — Scores were defined as: (1) death; (2) hospitalized, on mechanical ventilation or ECMO; (3) hospitalized, on non-invasive ventilation (BiPAP/CPAP and/or high-flow oxygen); (4) hospitalized, requiring oxygen; (5) hospitalized, not requiring oxygen; (6) not hospitalized, with limitation; and (7) not hospitalized, without limitations. Abbreviations: HCQ/CQ, hydroxychloroquine or chloroquine; NIV, noninvasive ventilation.

Table 2. Primary, secondary, and safety outcomes, overall and by trial.

	Overall (n = 770)		ORCHID (n = 479)		TEACH (n = 128)		HAHPS (n = 85)		WU352 (n = 30)	NCT04344444 (n = 20)		OAHU-COVID19 (n = 16)		NCT04335552 (n = 11)		COVID MED (n = 1)	Missing
Primary outcome: Ordinal scale improvement under HCQ/CQ at day 28–35 ^a
Model-standardized proportional OR (95% CrI)	0.97 (0.76 to 1.24)		0.96 (0.74 to 1.23)		1.00 (0.68 to 1.59)		0.92 (0.61 to 1.36)		1.01 (0.51 to 1.72)	0.83 (0.52 to 1.59)		0.76 (0.38 to 1.30)		1.27 (0.66 to 2.44)		1.26 (0.23 to 4.03)	NA
Plug-in proportional OR (95% CI)	0.98 (0.75 to 1.28)		1.02 (0.73 to 1.43)		0.75 (0.35 to 1.60)		0.81 (0.36 to 1.81)		NA	0.31 (0.01 to 2.85)		0.33 (0.04 to 2.30)		1.38 (0.11 to 18.47)		NA	36 (4.7%)
Secondary outcomes ^a
Mortality at day 28–35 under HCQ/CQ vs control, model-standardized RD (95% CrI)	-0.01 (-0.04 to 0.02)		-0.01 (-0.04 to 0.02)		0.00 (-0.04 to 0.05)		-0.03 (-0.07 to 0.05)		0.00 (-0.02 to 0.03)	-0.02 (-0.07 to 0.04)		-0.04 (-0.12 to 0.02)		0.01 (-0.03 to 0.08)		0.01 (-0.16 to 0.09)	NA
Mortality at day 28–35 under HCQ/CQ vs control, plug-in RD (95% CI)	-0.01 (-0.06 to 0.04)		0.00 (-0.05 to 0.06)		-0.01 (-0.16 to 0.14)		-0.12 (-0.26 to 0.02)		NA	0.13 (-0.37 to 0.64)		-0.20 (-0.58 to 0.18)		-0.08 (-0.73 to 0.57)		NA	36 (4.7%)
	Control (n = 358)	HCQ/CQ (n = 412)	Control (n = 237)	HCQ/CQ (n = 242)	Control (n = 61)	HCQ/CQ (n = 67)	Control (n = 43)	HCQ/CQ (n = 42)	HCQ/CQ (n = 30)	Control (n = 5)	HCQ/CQ (n = 15)	Control (n = 6)	HCQ/CQ (n = 10)	Control (n = 5)	HCQ/CQ (n = 6)	Control (n = 1)	Missing
Days of hospitalization between enrollment and day 28 (median)	7	7	8	7	5	5	6	6.5	4	2	9	9	18	8.5	4.5	9	1
Patients on mechanical ventilation between enrollment and day 28, No. (%)	76 (21)	82 (20)	58 (24)	51 (21)	4 (7)	7 (10)	11 (28)	14 (35)	3 (10)	0 (0)	1 (7)	2 (33)	5 (50)	1 (25)	1 (17)	0 (0)	7
Safety outcomes
Adverse events (AEs), count (per patient)	104 (0.29)	160 (0.39)	39 (0.16)	50 (0.21)	59 (0.97)	63 (0.94)	2 (0.05)	3 (0.07)	29 (0.97)	1 (0.20)	8 (0.53)	3 (0.50)	7 (0.70)	NA	NA	0	11
Serious adverse events (SAEs), count (per patient)	32 (0.09)	53 (0.13)	12 (0.05)	18 (0.07)	11 (0.18)	14 (0.21)	0	0	9 (0.30)	1 (0.25)	4 (0.27)	2 (0.33)	4 (0.40)	6 (1.20)	4 (0.67)	0	1
QTc prolongation AEs, count (per patient)	8 (0.02)	14 (0.03)	3 (0.01)	2 (0.01)	1 (0.02)	3 (0.04)	2 (0.05)	3 (0.07)	2 (0.07)	1 (0.20)	3 (0.20)	1 (0.17)	1 (0.10)	NA	NA	0	11
QTc prolongation SAEs, count (per patient)	1 (0.00)	1 (0.00)	0	0	0	1 (0.01)	0	0	0	0	0	1 (0.17)	0	NA	NA	0	11
Elevated LFTs AEs, count (per patient)	4 (0.01)	21 (0.05)	3 (0.01)	12 (0.05)	0	1 (0.01)	0	0	0	0	7 (0.47)	1 (0.17)	1 (0.10)	NA	NA	0	11
Elevated LFTs SAEs, count (per patient)	0	0	0	0	0	0	0	0	0	0	0	0	0	NA	NA	0	11
Arrhythmia AEs, count (per patient)	10 (0.03)	8 (0.02)	10 (0.04)	1 (0.00)	0	0	0	0	2 (0.07)	0	4 (0.27)	0	1 (0.10)	NA	NA	0	11
Arrhythmia SAEs, count (per patient)	3 (0.01)	1 (0.00)	3 (0.01)	0	0	0	0	0	0	0	1 (0.07)	0	0	NA	NA	0	11
Missingness in the primary outcome (ordinal scale between day 28–35)
Nonmissing primary outcome, No. (%)	342 (96)	392 (95)	237 (100)	242 (100)	47 (77)	50 (75)	42 (98)	42 (100)	27 (90)	5 (100)	15 (100)	6 (100)	10 (100)	4 (80)	6 (100)	1 (100)	NA

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^aPositive odds ratios and risk differences favor HCQ/CQ over control.

Abbreviations: 95% CrI, 95% credible intervals; HCQ/CQ, hydroxychloroquine or chloroquine; LFTs, liver function tests; NA, not applicable; OR, odds ratio; RD, risk difference.

Fig 3 — Estimated proportional odds ratios comparing day 28–35 ordinal scale in HCQ/CQ versus control groups. Estimates are given for the pooled patient population and for subgroups. Blue circles represent model-standardized estimates; blue horizontal lines represent 95% credible intervals. Open grey circles represent plug-in estimates; grey horizontal lines represent 95% confidence intervals. Grey circle size represents the number of patients in the corresponding subgroup. Arrows indicate uncertainty intervals extending beyond plot limits. Study acronyms are explained in Fig 1. Two trials did not have study acronyms (only trial registration numbers). Abbreviations: 95% CrI, 95% credible intervals; HCQ/CQ, hydroxychloroquine or chloroquine; NA, not applicable; NIV, noninvasive ventilation (includes BiPAP/CPAP and/or high-flow oxygen); OR, odds ratio.

Mortality: Pooled and subgroup analysis

Mortality at 28–35 days was similar in HCQ/CQ vs control groups (10%, n = 43 HCQ/CQ vs 9%, n = 34 control; model-adjusted risk difference [RD], -0.01 [95% CrI, -0.04 to 0.02]; plug-in RD, -0.01 [95% CI, -0.06 to 0.04], where a positive RD favors HCQ/CQ) (Fig 4). Again, we observed no appreciable heterogeneity in treatment effect estimates across prespecified subgroups. On the RD scale, there was greater uncertainty about the effect of HCQ/CQ upon mortality for those with higher baseline risk scores. For those with low baseline risk scores, the model precisely predicts only small effects of HCQ/CQ (RD for the first group, -0.01 [95% CrI, -0.02 to 0.01]; second group, -0.01 [95% CrI, -0.02 to 0.01]; third group, 0.00 [95% CrI, -0.02 to 0.02]; fourth group, -0.01 [95% CrI, -0.05 to 0.03]; and fifth group, -0.03 [95% CrI, -0.15 to 0.07]) (Fig 4). Separate estimates of mortality at day 28–35 under control and HCQ/CQ are shown in S3 Fig, with conditional effect analyses in S2 Fig.

Fig 4 — Estimated risk differences for day 28–35 mortality in HCQ/CQ versus control groups. Estimates are given for the pooled patient population and for subgroups. Blue circles represent model-standardized estimates; blue horizontal lines represent 95% credible intervals. Open grey circles represent plug-in estimates; grey horizontal lines represent 95% CIs. Grey circle size represents the number of patients in the corresponding subgroup. Arrows indicate uncertainty intervals extending beyond plot limits. Study acronyms are explained in Fig 1. Two trials did not have study acronyms (only trial registration numbers). Abbreviations: 95% CrI, 95% credible intervals; HCQ/CQ, hydroxychloroquine or chloroquine; NA, not applicable; NIV, noninvasive ventilation (includes BiPAP/CPAP and/or high-flow oxygen); OR, odds ratio.

Sensitivity analyses

Our alternative models and outcome definitions produced qualitatively similar conclusions about overall and subgroup effects. Simpler models than the one we prespecified (removing interactions, adding weakly informative priors) had better leave-one-out cross-validation performance than our primary prespecified model, while the model fit only to ORCHID data performed worse than our primary model (S2 Appendix). Our primary model fit without individual-level treatment-covariate interactions yielded an adjusted OR of 0.92 (95% CrI, 0.58–1.41) for the benefit of HCQ/CQ. Posterior predictive checks of our primary model indicated good in-sample fit (S4 Fig).

Exploratory analysis of secondary and safety outcomes

There were similar rates of mechanical ventilation between enrollment and day 28 (20% [n = 82] HCQ/CQ vs 21% [n = 76] control). HCQ/CQ and control patients had a median post-enrollment hospital length of stay of 7 days.

Overall AE rates were numerically higher in the HCQ/CQ vs the control group (0.39 vs 0.29 per patient, respectively), as were overall SAE rates (0.13 vs 0.09 per patient). These comparisons are potentially vulnerable to aggregation bias, but are broadly consistent with results from within each of the largest three studies (ORCHID, TEACH, and HAHPS). LFTs elevation AE rates were also numerically higher with HCQ/CQ (0.05 [n = 21] vs 0.01 [n = 4] per patient). QTc prolongation and arrhythmia AE and SAE rates were similar (Table 2).

Discussion

This IPD meta-analysis of 8 RCTs in 770 hospitalized COVID-19 patients comparing HCQ/CQ and control treatment confirms results of at least 16 published RCTs showing no benefit of HCQ/CQ [14, 16–30]. Neither the primary outcome measurement, ordinal scale at 28–35 days, nor the secondary outcome measurement, mortality at 28–35 days, was improved with HCQ/CQ in the pooled study population. We found no subgroup in which appreciable benefits could be observed for the primary outcome. Overall rates of AEs and SAEs, and elevated LFT AEs, but not QTc prolongation AEs, were higher with HCQ/CQ than controls.

This study adds value to the literature by synthesizing IPD from 8 RCTs, of which 7 were terminated early, 4 unpublished, and 4 published individually and/or in aggregate data meta-analyses (ORCHID, TEACH, HAHPS, and NCT04335552) [14, 22, 23, 39, 40]. To our knowledge, this is the first published meta-analysis of HCQ/CQ trials in hospitalized COVID-19 patients to use IPD rather than aggregate data. We are aware of only 4 additional published IPD meta-analyses for COVID-19 therapeutics (3 planned; 1 smaller one completed) [41–44]. Two meta-analyses planned to use IPD data to supplement aggregate data but could not obtain them [45, 46].

This analysis used a random effects model to synthesize IPD about the efficacy of HCQ/CQ overall and stratified by 6 key covariates. Pooling individual-level data allowed us to obtain relatively stable estimates of patient-level subgroup effects [47] that have only been minimally investigated in other published aggregate data meta-analyses [40, 46, 48].

We produced more precise estimates of the overall effects relative to a similar analysis based on ORCHID alone, which represented 62% of our study sample; subgroup effect estimates also tended to be more precise. Our model was flexibly specified, and our qualitative conclusions were robust to both alternative model specifications and varying outcome time windows.

It is instructive to consider our results in the context of these drugs’ history in COVID-19.

Inconsistent antiviral activity was reported for HCQ/CQ in vitro and in animal models against coronaviruses including SARS-CoV-2 [2, 49–51]. Early in the pandemic, faster SARS-CoV-2 shedding clearance was reported in small, mainly uncontrolled trials, prompting recommendations by some experts and guidelines to administer HCQ/CQ empirically in COVID-19 [1, 3, 52]. Based partly on these data, and safety in other indications, the FDA issued an HCQ/CQ EUA for hospitalized COVID-19 patients on March 28, 2020. The accelerated viral shedding clearance reported by Gautret et al. [3], however, could not be replicated [53].

Increased rates of QTc prolongation were reported from small trials, particularly with higher dosing and azithromycin co-administration, though torsades de pointes was rare [5–7, 54]. These reports and others prompted FDA issuance of a Drug Safety Communication on April 24, 2020, about HCQ/CQ “known risks” [55].

Subsequent retrospective-observational studies in hospitalized COVID-19 patients did not find evidence of benefit, some reporting potential harm [8–10, 15]. Additional mostly smaller retrospective-observational studies reported decreased mortality with HCQ monotherapy and with azithromycin [11–13, 56]. These studies were observational, without randomization or blinding, and with baseline imbalances and other potential sources of confounding.

In May 2020, the FDA published a pharmacovigilance memorandum on HCQ/CQ safety data [57]. QT prolongation and increased LFTs were the most common cardiac and non-cardiac SAEs, respectively. Our study similarly observed increased rates of LFTs elevation AEs with HCQ/CQ but not QTc prolongation. Case reports of HCQ-induced liver injury were published before and during the pandemic [58–60]. Exclusion of patients with prolonged QTc in all of our trials and of co-administration of additional QTc-prolonging medications in some may have averted QTc prolongation in our population.

Results from at least five RCTs became available in the spring/summer of 2020.

The first was a Chinese multicenter open-label RCT in 150 mild-moderate severity hospitalized COVID-19 patients [16]. Mean interval from symptom onset was 16.6 days (longer than our 6.2 days). The main outcome, 28-day nasopharyngeal swab conversion, was similar in HCQ (85.4%) versus standard care patients (81.3%). AEs occurred in 30% vs 9%; SAEs in 2 vs 0 patients, respectively. No arrhythmias or QTc prolongation occurred. The pattern of safety results in our study was similar.

Results from the open-label RECOVERY platform trial comparing HCQ and standard care in hospitalized COVID-19 patients were released publicly (with HCQ arm enrollment cessation) on June 5, 2020, and published soon after [17]. Median duration from symptom onset was 9 days (longer than our 6 days). RECOVERY patients were sicker than those in our study (17% vs 6% received mechanical ventilation and/or ECMO). The trial’s primary outcome, 28-day mortality, occurred in 421 of 1,561 (27%) HCQ vs 790 of 3,155 (25%) standard care patients (rate ratio, 1.09; 95% CI, 0.97–1.23). No evidence of benefit was reported in any subgroup. Adverse safety signals included higher cardiac death rates but not arrhythmias. Our efficacy overall and subgroup results were similar. Results from RECOVERY (and other emerging data) led the FDA on June 15, 2020, to revoke its EUA for HCQ/CQ.

ORCHID, one of the studies in this IPD meta-analysis, was a double-blind, placebo-controlled trial of HCQ in 479 hospitalized COVID-19 patients. On June 20, 2020, the NIH announced discontinuation of enrollment after the data and safety monitoring board “determined that while there was no harm, the study drug was very unlikely to be beneficial” [61]. Results were published on November 9, 2020 [14]. Median interval from symptom onset was 5 days (similar to our pooled cohort). The primary outcome, the WHO 14-day ordinal score, was similar between HCQ vs placebo (adjusted proportional OR, 1.02; 95% CI, 0.73–1.42), as was the 28-day score (adjusted proportional OR, 1.07; 95% CI, 0.54–2.09). No evidence of benefit was reported for any subgroup. Overall AE (21% vs 16%), overall SAE (7% vs 5%), and QTc prolongation (5.9% vs 3.3%) rates were numerically higher with HCQ. Our safety results are similar, except for QTc prolongation. Our efficacy analysis focuses on a different estimand but produced qualitatively similar conclusions. Our primary analysis produced a comparably precise estimate of the overall effect of HCQ on day 28–35 ordinal scale compared with a similar analysis using a model fit to ORCHID data alone (OR 1.00; CrI 0.79 to 1.26). Subgroup effects from the full-data model tended to be more precise than those from the ORCHID-only model.

A July 4, 2020, WHO press release stated that its open-labeled SOLIDARITY trial (fourth RCT) would stop enrollment in its HCQ arm after an interim analysis showed little or no reduction in mortality; safety signals were also detected [62]. Results were published on December 2, 2020 [19]. Baseline ordinal score distribution was similar to our study. The primary outcome, in-hospital mortality, occurred in 104 of 947 (11.0%) HCQ vs 84 of 906 (9.3%) control patients (rate ratio, 1.19; 95% CI, 0.89–1.59).

Coalition Covid-19 Brazil I was a multicenter, open-label trial that compared HCQ, HCQ and azithromycin, and standard care in 504 mild-moderate severity hospitalized COVID-19 patients [18]. HCQ was started at a median of 7 days after symptom onset (similar to our study). The primary outcome, a 15-day ordinal scale, was not significantly different for HCQ or HCQ/azithromycin versus standard care. QTc prolongation and LFTs elevation were more common with HCQ (the latter in line with our study).

Updated NIH and Infectious Diseases Society of America COVID-19 guidelines (June 2020) recommended against HCQ/CQ use except in clinical trials [63, 64].

Eleven additional RCTs assessing HCQ/CQ in hospitalized patients with COVID-19 were published later in the pandemic (winter 2020 and 2021). All showed no substantial treatment benefit or worse primary outcomes, including clinical ordinal scales, mortality, composite scores, and viral shedding; some showed adverse safety signals—increased QTc prolongation, acute kidney injury, and AEs/SAEs [20–30].

At least 50 aggregate data meta-analyses evaluating HCQ/CQ in hospitalized COVID-19 patients have been published, with the overwhelming majority finding lack of evidence of convincing clinical benefit, and many finding worse clinical outcomes and increased AE and SAE rates [40, 46]. In contrast with our IPD meta-analysis, these studies included either no or minimal patient-level subgroup analyses.

For other COVID-19 populations (outpatients, prophylaxis), most studies did not find evidence of HCQ benefit [65–76].

The results of our study are congruous with the thrust of HCQ/CQ studies thus far, which have showed equivocal preclinical efficacy and no convincing evidence of clinical efficacy, as well as adverse safety signals, in the overwhelming majority of retrospective-observational studies, RCTs, and meta-analyses [5–30].

Our study has some key limitations. First, we included trials with open-label designs and varying treatments (HCQ vs CQ; with and without azithromycin). Second, 6 studies had some risk of bias. Third, we pooled a limited set of studies identified in our searches because some principal investigators declined participation and we excluded international trials. Fourth, we made SAP modifications after PROSPERO registration (S3 Table). Fifth, our analysis combined HCQ and CQ arms; only 16 patients received CQ alone. Sixth, in our safety analysis we did not harmonize AE and SAE definitions among the included studies. Seventh, our final study search was conducted in June 2020. This timeline is consistent with what is typical for IPD meta-analyses, which commonly take upwards of two years to complete [31]. Repeating our searches of ClinicalTrials.gov in July 2022 and applying the same eligibility criteria revealed two unpublished studies (NCT04429867 and NCT04458948) potentially eligible for inclusion registered in June and July of 2020, respectively. (Registrations of new trials of hydroxychloroquine for COVID-19 had slowed substantially by August 2020 [77].

Conclusions

Our IPD meta-analysis confirms published results from retrospective-observational studies, RCTs, and aggregate data meta-analyses showing no strong evidence of efficacy, but concerning safety signals, for hydroxychloroquine (or chloroquine) use overall and in prespecified subgroups of hospitalized COVID-19 patients.

Supporting information

S1 Appendix. PRISMA-IPD checklist of items to include when reporting a systematic review and meta-analysis of individual participant data (IPD).

(PDF)

Click here for additional data file.^{(215.9KB, pdf)}

S2 Appendix. Methods supplement: Description of the primary outcome model and estimands.

(PDF)

Click here for additional data file.^{(378.6KB, pdf)}

S3 Appendix. Search strategy in detail.

(PDF)

Click here for additional data file.^{(305.1KB, pdf)}

S1 Table. Data dictionary from data harmonization spreadsheet.

(PDF)

Click here for additional data file.^{(193.7KB, pdf)}

S2 Table. Risk of bias assessment.

(PDF)

Click here for additional data file.^{(163.3KB, pdf)}

S3 Table. Changes to the prespecified statistical analysis plan.

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S4 Table. Primary, secondary, and safety outcomes.

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S5 Table. Trial characteristics: Treatment groups, participant assessment, and inclusion/exclusion criteria.

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S6 Table. Merging trial arms.

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S1 Fig. Assessing between-study heterogeneity.

Estimated study coefficients from the primary outcome model, with 66% and 95% credible intervals (CrI) indicated. “Control” terms represent differences in predicted outcomes between sites under control, for individuals with similar baseline covariates, on the proportional log-odds scale. Treatment effect terms represent differences in predicted treatment effect between sites, again for individuals with similar baseline covariates. Positive coefficients represent, respectively, better ordinal scale outcomes under control and benefit of HCQ/CQ at days 28–35 post-enrollment. Estimated between-study standard deviations were 1.48 for the “Control” terms (95% CrI, 0.16–5.98; on the log-odds scale) and 0.87 for the treatment effects (95% CrI, 0.01–5.17). Tests of publication bias applied to unadjusted study-specific proportional ORs from the 6 studies with patients assigned to both HCQ/CQ and control yielded inconclusive results (Egger’s test: p = 0.10; Begg’s test: p = 0.57); these should be interpreted with caution given the small number of included studies. HCQ/CQ indicates hydroxychloroquine or chloroquine.

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S2 Fig. Conditional covariate effects.

Conditional effects from the Bayesian proportional odds model. Shown are (1) the relative risk of mechanical ventilation or death at day 28–35; (2) the estimated probabilities of mechanical ventilation or death at day 28–35 under control and HCQ/CQ; and (3) the log proportional odds ratio comparing HCQ/CQ and control. Each of these effects are shown for reference individuals with the following covariate values: age 60, BMI 25, no baseline comorbidities, baseline ordinal score of 5, and sex coefficient set between male and female values. Curves for continuous covariates are accompanied by 50% and 95% credible bands; intervals for discrete covariates are accompanied by 66% and 95% credible intervals. BMI indicates body mass index; HCQ/CQ, hydroxychloroquine or chloroquine.

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S3 Fig. Estimated mortality rate in subgroups under both control and HCQ/CQ.

Shown are both plug-in estimates (based on the proportion of deaths in each subgroup) along with 95% CIs, and model-adjusted estimates with 95% credible intervals. The model used is the same as for the primary outcome analysis. HCQ/CQ indicates hydroxychloroquine or chloroquine.

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S4 Fig. Posterior predictive check of primary outcomes by study.

Shown are observed outcome data (first row) and draws for the posterior predictive distribution (subsequent rows) of the ordinal outcome scale at day 28–35, plotted against the expected linear predictor for each individual. Each column corresponds to one study in our analysis. Data points have been jittered for clarity.

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S5 Fig. Exploratory analysis of time between symptom onset and enrollment.

Post-hoc exploratory analysis of subgroups based on time between symptom onset and enrollment. Subgroups are based on approximate tertiles. Shown are (A) proportional odds ratios from models fit by maximum likelihood within each subgroup, with 95% CIs; (B) the empirical risk of survival for each treatment group within each subgroup, with 95% CIs; and (C) empirical risk differences within each subgroup, with 95% CIs. HCQ/CQ indicates hydroxychloroquine or chloroquine.

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Acknowledgments

The names of our nonauthor collaborators, including investigators and support staff for the trials analyzed, are listed below. We thank Emily Bartlett of Johns Hopkins University for manuscript preparation assistance. We also are grateful for the many contributions made by members of the Trial Innovation Network and the COVID-19 Collaboration Platform. This collaboration is based on research using data from TEACH, HAHPS, WU352, NCT04344444, OAHU-COVID19, NCT04335552, and COVID MED that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication.

The following individuals within the Pandemic Response COVID-19 Research Collaboration Platform for HCQ/CQ Pooled Analyses were instrumental in the planning and conduct of this study at each of the participating institutions:

ORCHID (NCT04332991): Derek C. Angus, MD, MPH; Alexandra Weissman, MD, MPH; Donald M. Yealy, MD (University of Pittsburgh, Pittsburgh, Pennsylvania). Roy G. Brower, MD (Johns Hopkins University School of Medicine, Baltimore, Maryland). Samuel M. Brown, MD, MS; Lindsay M. Leither, DO (Intermountain Medical Center and University of Utah, Salt Lake City, Utah). Jonathan D. Casey, MD, MSc; Sean P. Collins, MD; Todd W. Rice MD (Vanderbilt University Medical Center, Nashville, Tennessee). Steven Y. Chang, MD, PhD (UCLA, Los Angeles, California). John C. Eppensteiner, MD (Duke University, Durham, North Carolina). Michael R. Filbin, MD; Douglas L. Hayden, PhD; David A. Schoenfeld, PhD; B. Taylor Thompson, MD; Christine A. Ulysse, MS (Massachusetts General Hospital, Boston, Massachusetts). D. Clark Files, MD; Kevin W. Gibbs, MD (Wake Forest School of Medicine, Winston-Salem, North Carolina). Adit A. Ginde, MD, MPH; Marc Moss, MD (University of Colorado School of Medicine, Aurora, Colorado). Michelle N. Gong, MD, MS (Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York). Frank E. Harrell, Jr, PhD; Christopher J. Lindsell, PhD (Vanderbilt University School of Medicine, Nashville, Tennessee). Catherine L. Hough, MD, MSc; Akram Khan, MD (Oregon Health and Science University School of Medicine, Portland, Oregon). Nicholas J. Johnson, MD; Bryce R.H. Robinson, MD, MS (University of Washington, Seattle, Washington). Michael A. Matthay, MD (University of California, San Francisco, California). Pauline K. Park, MD (University of Michigan, Ann Arbor, Michigan). Nathan I. Shapiro MD, MPH (Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts). Jay S. Steingrub, MD (University of Massachusetts Medical School-Baystate, Springfield, Massachusetts).

TEACH (NCT04369742): Jonathan S. Austrian, MD, NYU; Ellie Carmody, MD, MPH; Camila Delgado PhD; Yanina Dubrovskaya, PharmD, BCIDP; Jaishvi Eapen, MD; Brooklyn Henderson, RN; Alexander Hrycko, MD; Morris Jrada, MD; Yi Li, PhD; Prithiv J. Prasad, MBBS; Vanessa Raabe, MD; Gabriel A. Robbins, MD; Andrea B. Troxel, PhD (NYU Grossman School of Medicine, New York, New York). Martin Bäcker, MD; Dinuli Delpachitra, MBBS (NYU Long Island School of Medicine, Mineola, New York). Jack A. DeHovitz, MD, MPH, MHCDS, FACP (State University of New York Downstate Health Sciences University, Brooklyn, New York).

HAHPS (NCT04329832): Jacki Anderson; Brent Armbruster, BS; Valerie Aston, MBA; Katie Brown, BSN; Allison M Butler, MS; Briana Crook, BS; Diana Grant, BSN; Danielle Groat, PhD; Prathyusha Kodakandla; Naresh Kumar, MPH; Lindsay Leither, DO; Heather Maestas, BS; Mardee Merrill, BS; Amanda Nelson; Rilee Smith, MPH; Rajendu Srivastava, MD, MPH; Nathan Starr, DO; Brandon J Webb, MD (Intermountain Healthcare, Murray, Utah). Macy Barrios, BS; Tom Greene, PhD; Jorden Greer, BS; Colin K Grissom, MD; Benjamin Haaland, PhD; Estelle Harris, MD; Stacy Johnson, MD; Robert Paine III, MD; Ithan Peltan, MD, MSc; Lisa Weaver, BS; Jian Ying, PhD (University of Utah, Salt Lake City, Utah).

WU352 (NCT04341727): Mansi Agarwal; Megan Arb; Teresa Arb; Andrea Patterson Brown; Jennifer Bruns; Kelly Caplice; PeChaz Clark; Rachel Cody; Brittany Depp; Charles Goss; Zackary Jakuboski; Michael Klebert; Kathleen McNulty; Tina Nolte; Arnita Pitts; Rachel Presti; Shea Roesel-Wakeland; Tara Sattler; Andrej Spec; Heather Wilkins; Brittney Zwijack (Washington University School of Medicine, St Louis, Missouri). Kristopher Bakos; Kathryn Vehe (Barnes-Jewish Hospital, St Louis, Missouri).

NCT04344444: Andrew Chapple, PhD; Michael Hagensee, MD, PhD; Amber Trauth, MPH; Brianne Voros, MS (Louisiana State University Health Sciences Center, New Orleans, Louisiana). Jyotsna Fuloria, MD (University Medical Center-New Orleans, New Orleans, Louisiana).

OAHU-COVID19 (NCT04345692): Todd B. Seto, MD (The Queen’s Medical Center, Honolulu, Hawaii).

NCT04335552: Arthur W. Baker, MD, MPH; Thuy Le, MD, PhD; Ahmad Mourad, MD; Susanna Naggie, MD, MHS; Shanti Narayanasami, MBBS; Sean M. O’Brien, PhD; Nwora L. Okeke, MD, MPH; Frank W. Rockhold, PhD; Robert J. Rolfe Jr., MD; Nicholas A. Turner, MD, MHS; Rebekah Wrenn, PharmD (Duke University Medical Center, Durham, North Carolina).

COVID MED (NCT04328012): Anne M. Gadomski, MD, MPH, FAAP (Bassett Research Institute and Bassett Medical Center, Cooperstown, NY).

Data Availability

The ORCHID trial data underlying the results presented in the study are available from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center (https://biolincc.nhlbi.nih.gov/; accession number HLB02372021a). The data for the other studies presented are available from Vivli (https://www.vivli.org): COVID MED, https://doi.org/10.25934/00006535; HAHPS, https://doi.org/10.25934/00006626; NCT04335552, https://doi.org/10.25934/00006861; NCT04344444, https://doi.org/10.25934/00006865; OAHU-COVID19, https://doi.org/10.25934/00006595; TEACH, https://doi.org/10.25934/00006627; and WU352, https://doi.org/10.25934/00006713. Policies for accessing these third-party datasets vary somewhat by study and repository, but requests must be approved and require a signed agreement.

Funding Statement

This work was supported by the National Institutes of Health (NIH) National Center for Advancing Translational Sciences (grant U24 TR001609, awarded to D.F.H.). L.D.S. is supported by an American Australian Association Sir Keith Murdoch Scholarship. E.L.O. is supported by the Johns Hopkins Bloomberg School of Public Health. S.M.B. and W.R.B. were funded by the Intermountain Research and Medical Foundation, Intermountain Heart and Lung Foundation, and Intermountain Office of Research for the HAHPS study. J.A.O. and the WU352 study were funded by an NIH Clinical and Translational Science Award (grant UL1 TR002345) through the Washington University Institute of Clinical and Translational Sciences. W.H.S. and the ORCHID trial were supported by the National Heart, Lung, and Blood Institute (grants 3U01 HL123009-06S1, U01 HL123009, U01 HL122998, U01 HL123018, U01 HL123023, U01 HL123008, U01 HL123031, U01 HL123004, U01 HL123027, U01 HL123010, U01 HL123033, U01 HL122989, U01 HL123022, and U01 HL123020). Sandoz, a Novartis division, supplied the hydroxychloroquine and placebo used in the ORCHID trial. J.E.S. was funded by Duke University School of Medicine for the NCT04335552 study. J.V. and D.F. are supported by the Bassett Research Institute. B.E.B. is supported by the National Center for Advancing Translational Sciences (grant UL1 TR002541) and Brigham and Women’s Hospital. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0273526.r001

Decision Letter 0

A M Abd El-Aty

27 May 2022

PONE-D-22-06839Hydroxychloroquine/Chloroquine for the Treatment of Hospitalized Patients with COVID-19: An Individual Participant Data Meta-AnalysisPLOS ONE

Dear Dr. Freilich,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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The authors have read the journal’s policy and have the following competing interests: SNB, NM, MRC, and DFH reported receiving research funding from the Department of Defense for clinical trials of convalescent plasma for COVID-19 outside the submitted work. NAG reported receiving salary support from the National Institutes of Health (NIH) National Center for Advancing Translational Sciences via a Johns Hopkins Clinical and Translational Science Award outside the submitted work. YB reported being a site investigator for Janssen outside the submitted work. SMB reported service as chair of a data and safety monitoring board for a Hamilton clinical trial in respiratory failure; fees paid to Intermountain Healthcare from Faron Pharmaceuticals and Sedana Pharmaceuticals for steering committee service for a clinical trial in acute respiratory distress syndrome; research grants to Intermountain Healthcare from Janssen, NIH, Centers for Disease Control and Prevention, and Department of Defense; and royalties from Oxford University Press and Brigham Young University, outside the submitted work. MEC reported service on a Roche advisory board and as a site investigator for Janssen outside the submitted work. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials. All other authors report no conflicts of interest.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Partly

Reviewer #4: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thanks for inviting me to reviewing this paper. This is a high-quality IPD meta-analysis. I especially enjoy the statistical methods used for the analysis. Although the topic has already been reported by several other meta-analyses, it still brings value through the individual participant data perspective— the evidence could be ranked at the top over previous similar meta-analyses.

I only have two questions, about the safety assessment. I see the author lists and found there some renewed methodologist in safety assessment. They may have their own consideration. But I think a further investigation may makes the paper more attractive.

1. First, the authors seem failed to define the AEs and SAEs clearly. Ambiguous definition raises confusions during data extraction and impacts the transparency as well as reproducibility.

2. Second, the authors reported the adverse events outcomes descriptively, this is not the best solution because without weighting scheme the results tend to at risk of Simpson’s paradox. To avoid this, I suggest the authors statistically analysis the safety outcomes, for example, the GLMM/Beta-Binominal model.

Reviewer #2: I read the manuscript carefully. It’s a well-written and robust manuscript only there are some minor comments.

Minor comments:

1-Please add reference for PRISMA guide line.

2- the authors claimed that their study stick to PRISMA guide line. However, based on this protocol, search strategy should be presented in text or supplementary file. Also, PRISMA check list is necessary.

Reviewer #3: Dear Editor,

I carefully read the manuscript by Di Stefano et al.

My comments and suggestions for the authors are the following:

- The search process should be updated (this is a critical issue!).

- The authors mention that their meta-analysis is PRISMA compliant. However, they should specify the version of PRISMA guidelines they referred to and include the article among the references of the manuscript.

- Line 212: Including in a meta-analysis data from a personal communication is not a recomendable and increase the risk of publication biases for the analysis.

- The authors should perform also Begg and Egger's tests.

- The authors should consider to refer to doi: 10.1093/ehjcvp/pvaa105 in the discussion of the manuscript.

Reviewer #4: The authors present a very interesting IPD meta-analysis based on 8 RCTs including unanalyzed data from early trials on the efficacy and safety of HCQ/CQ in COVID-19 prevention and treatment. The paper and analysis are overall of very good quality. I suggested some minors restructuration and additional subgroup analysis that could help strengthen the authors’ claim.

Abstract

Authors did not mention what was the primary outcome in the method (they just mentioned the metric of the scale). It is important to know upfront the main measure of their work (at least the conceptual framing behind it). The authors mentioned again the “COVID-19 ordinal scores” in results without giving a specific measure in the abstract. They could conclude on the potential harming effects of HCQ/CQ.

Introduction

I really appreciate the transparency protocol the authors put into place (though it would be great to have the exact link for Vivli and CCP repositories to be able to consult the database). Outcomes are better explained in the main text; however, authors could justify why they used these specific levels. Was this recommendation base on the studies they re-analyzed? Or is this just a general classification recommended by health authorities and authors had to recode the whole thing? Precisions may help in that part. Also, authors mentioned a change in the outcome window due to missing data. How did the authors handle missing data in their study?

PLOS One is a very generalist journal so it would be great if the authors could recall the strength-weakness of RCTs vs. observational studies for a general audience as well as the benefits of randomization and how it helps to make causal inferences (i.e., controlling for confounding, random sampling criteria, etc. they could cite Schulz et al., 2002 or Vandenbroucke, 2004 for instance). This could also help strengthen the theoretical part of the paper which is unbalanced compared to the other sections of the paper.

Results

The authors mentioned early on that, due to missing data, they had to broaden days of post enrollment from 28-30 to 28-35. It would still be interesting though to check whether the main results changed in function of this amendment and how the authors can justify days 35 as the cut-off and not 34 or 33.

The authors make a compelling case of justifying many sub-group analyses to explore whether the HCQ/CQ presented local benefits for age, gender, BMI, etc. subgroups. Another possible and important subgroup is the timing of the medication. Some authors (see Prodomos & Rumschlag, 2020 or Million et al., 2020) have argued that HCQ is only effective when provided earlier, not associated with worsening disease and safe. Although I’m aware this argument is very borderline, if the authors have enough studies to compared early vs. late treatment, they would be able to respond to those critics and relativize some open-access analysis that claim early treatment showed better improvement (https://c19hcq.com/).

Discussion

The discussion part is much more detailed than the introduction. The authors provide a review of RCTs history on HCQ and COVID-19. I would recommend moving some parts of the discussion to the introduction to better contextualize the debate on HCQ and help naive readers understand what’s at stake and why their work is important.

The Annex and online supplementary materials are very complete and detailed.

**********

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Reviewer #1: Yes: Chang Xu

Reviewer #2: Yes: Bahman Razi

Reviewer #3: No

Reviewer #4: Yes: Jordane Boudesseul

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Attachment

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PLoS One. 2022 Sep 29;17(9):e0273526. doi: 10.1371/journal.pone.0273526.r002

Author response to Decision Letter 0

11 Jul 2022

Response to Academic Editor and Reviewers

Thank you for giving us the opportunity to revise our manuscript, “Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis.”

We sincerely appreciate the thorough and knowledgeable review. We have responded to the editor and reviewer concerns here and have tracked our edits in the revised files. We hope that these responses are satisfactory and that the revised manuscript will be acceptable for publication in PLOS ONE.

Journal Requirements

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: We have reviewed the templates to ensure our manuscript meets PLOS ONE’s style requirements, and in particular have checked our file names.

2. Thank you for stating the following in the Competing Interests section:

Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

Response: We note that this sentence was included already as the second-last sentence of our Competing Interests statement, as quoted above: “This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.” However, we have changed “the authors’” to “our” and removed the sentence that followed (“All other authors report no conflicts of interest”). We have included the updated Competing Interests statement in our cover letter as requested.

Response: This work has been posted to the medRxiv preprint server (DOI: 10.1101/2022.01.10.22269008), and aspects of the work were presented at the Methods in Evidence Synthesis Salon at the University of Bristol. As is typical in meta-analyses and as noted in our cover letter, some of the included studies have been published previously. The meta-analysis itself has not otherwise been published or presented.

Response: We note that the consortium (Pandemic Response COVID-19 Research Collaboration Platform for HCQ/CQ Pooled Analyses) comprises nonauthor collaborators, as defined by ICMJE. In accordance with ICMJE recommendations and MEDLINE policies (https://www.nlm.nih.gov/bsd/policy/authorship.html), we included the consortium in the byline to ensure the collaborators will be indexed appropriately. We have moved the names and affiliations of the nonauthor collaborators in the consortium from the supplementary appendix to the acknowledgments section of the manuscript. If a contact person for the group is still needed, Anne Gadomski can be contacted at anne.gadomski@bassett.org.

http://journals.plos.org/plosone/s/submission-guidelines#loc-reference-style

Response: We have been unable to find “Bewick et al” in our manuscript or our reference list. From your excerpt, it appears to be citation #5, which was Borba et al, published in 2020. We will gladly remove any unpublished citations but are unclear to which you refer.

Reviewer Comments to the Author

Reviewer #1

Thanks for inviting me to reviewing this paper. This is a high-quality IPD meta-analysis. I especially enjoy the statistical methods used for the analysis. Although the topic has already been reported by several other meta-analyses, it still brings value through the individual participant data perspective— the evidence could be ranked at the top over previous similar meta-analyses.

Response: We thank Reviewer #1 for their positive comments on our analysis and its value.

R1: I only have two questions, about the safety assessment. I see the author lists and found there some renewed methodologist in safety assessment. They may have their own consideration. But I think a further investigation may makes the paper more attractive.

1. First, the authors seem failed to define the AEs and SAEs clearly. Ambiguous definition raises confusions during data extraction and impacts the transparency as well as reproducibility.

Response: Thank you for pointing out the potential for ambiguity regarding our definitions of AEs and SAEs. Our AE/SAE definitions were based on each individual protocol’s AE and SAE definitions. We considered harmonizing our AE/SAE definitions across studies but decided against it because this would have required extensive manual chart review by participating trialists.

We believe that our descriptive analysis remains interpretable and useful to readers because most trialists use standard AE/SAE definitions (most commonly, Common Terminology Criteria for Adverse Events [CTCAE]) so that heterogeneity of definitions across trials is likely to be low.

Notwithstanding this, we have endeavored to clarify this limitation by making the following additions to the manuscript. In the Methods, we added the sentence: “Due to practical constraints, we did not attempt to synchronize adverse event definitions across the included studies.”

In the Discussion, we have added the following limitation: “Sixth, in our safety analysis we did not harmonize AE and SAE definitions among the included studies.”

R1: 2. Second, the authors reported the adverse events outcomes descriptively, this is not the best solution because without weighting scheme the results tend to at risk of Simpson’s paradox. To avoid this, I suggest the authors statistically analysis the safety outcomes, for example, the GLMM/Beta-Binominal model.

Response: We decided not to analyze the safety data using statistical models for the sake of simplicity, particularly given the non-harmonized event definitions, potential differences in safety monitoring and length of follow-up among the studies, and low numbers of events in the smaller studies. Adverse events are commonly presented in an exploratory way in clinical trials, and the case has been made that this is often the appropriate approach (Huster 1991; doi:10.1177/009286159102500315). However, if this explanation is unsatisfactory, we would be happy to conduct a statistical analysis at the editor’s request.

We agree that our pooled results are potentially vulnerable to aggregation bias/Simpson’s paradox. However, we also presented within-study comparisons of safety outcomes, which are not subject to this concern, and our aggregate results are consistent with the within-study from the largest studies. We have added a statement clarifying this to the Results: “These comparisons are potentially vulnerable to aggregation bias, but are broadly consistent with results from within each of the largest three studies (ORCHID, TEACH, and HAHPS).”

Reviewer #2

I read the manuscript carefully. It’s a well-written and robust manuscript only there are some minor comments.

Minor comments:

1. Please add reference for PRISMA guide line.

Response: We thank Reviewer #2 for their helpful comments on ensuring clarity of our use of the PRISMA guidelines. We used the PRISMA extension for individual patient data (PRISMA-IPD). We have updated the manuscript text to clarify this and have added a reference to the PRISMA-IPD statement paper (doi:10.1001/jama.2015.3656).

The manuscript has been updated in the final sentence of the Methods section as follows: “This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline extension for IPD analyses (PRISMA-IPD) [38].”

R2: 2. the authors claimed that their study stick to PRISMA guide line. However, based on this protocol, search strategy should be presented in text or supplementary file. Also, PRISMA check list is necessary.

Response: Our PRISMA-IPD checklist was included in our original submission as the S3 Appendix. To make it easier to find, we have made it the S1 Appendix so that it is the first item among our supporting information.

Our search strategy is presented briefly in the Methods section’s Trials Selection Summary, summarized in Fig 1, and outlined in greater detail in our supporting information. Our original manuscript presented the detailed description in S1 Fig: Trial Selection/RCT Selection Process in Detail, a more comprehensive flow diagram with a full page of text afterward that explained our process. We have relabeled that as S3 Appendix: Search Strategy in Detail and placed the text before the figure. Additionally, we have added a new table, Summary of Search Strategies, on the first page of the appendix that presents the search strategy more clearly and accessibly.

Reviewer #3

I carefully read the manuscript by Di Stefano et al. My comments and suggestions for the authors are the following:

1. The search process should be updated (this is a critical issue!).

Response: We thank Reviewer #3 for their comments and suggestions. We repeated our searches of ClinicalTrials.gov in July 2022. After filtering based on our eligibility criteria (i.e., excluding studies that were international, outpatient, had no enrollment, or were prophylactic), we found two additional potentially eligible studies: NCT04429867 (a randomized trial of hydroxychloroquine versus placebo) and NCT04458948 (a single arm combined study of azithromycin and hydroxychloroquine). Their study listings were last updated June 16 and July 7 of 2020, respectively (our most recent search was June 2, 2020). The first of these, NCT04429867, indicated they had no plans to share IPD.

Though two years between search and publication may be a long interval for traditional aggregate-data meta-analyses, this timeline is typical for IPD meta-analyses. For example, Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research by Riley et al (2021), states:

“Generally, IPD meta-analysis projects will take upwards of two years to complete, and sometimes longer depending on how many trials are involved and the complexities of negotiating collaboration, data coding, checking, cleaning and analysis” (p. 33) and “IPD meta-analysis projects are time-consuming, [...] often taking upwards of two years to engage with trial investigators; obtain, clean, harmonise and meta-analyse the IPD; and publish and disseminate results” (p. 294).

The Cochrane Handbook for Systematic Reviews of Interventions similarly states: “However, IPD reviews can take longer than other reviews; those evaluating the effects of therapeutic interventions typically taking at least two years to complete.” (Tierney JF, Stewart LA, Clarke M. Chapter 26: Individual participant data. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA [editors]. Cochrane Handbook for Systematic Reviews of Interventions version 6.1 [updated September 2020]. Cochrane, 2020. https://training.cochrane.org/handbook/current/chapter-26)

The literature review in our Discussion section covers more recent studies, including meta-analyses.

We have added the following to the Limitations section of the Discussion to explain this: “Seventh, our final study search was conducted in June 2020. This timeline is consistent with what is typical for IPD meta-analyses, which commonly take upwards of two years to complete [31]. Repeating our searches of ClinicalTrials.gov in July 2022 and applying the same eligibility criteria revealed two unpublished studies (NCT04429867 and NCT04458948) potentially eligible for inclusion, registered in June and July of 2020, respectively. (Registrations of new trials of hydroxychloroquine for COVID-19 had slowed substantially by August 2020 [77].)”

R3: 2. The authors mention that their meta-analysis is PRISMA compliant. However, they should specify the version of PRISMA guidelines they referred to and include the article among the references of the manuscript.

Response: We used the PRISMA extension for individual patient data (PRISMA-IPD). We have updated the manuscript text to clarify this and have added a reference to the PRISMA-IPD statement paper.

Additionally, we have moved our PRISMA-IPD checklist from the S3 Appendix to S1 Appendix so it will be more visible.

R3: 3. Line 212: Including in a meta-analysis data from a personal communication is not a recomendable and increase the risk of publication biases for the analysis.

Response: Our search strategy was accompanied by extensive outreach efforts to the research community designed to encourage broad collaboration and data sharing in COVID studies. We wanted to be transparent about considering the study suggested via personal communication. However, our actual study selection was guided only by our ClinicalTrials.gov searches. As that study was not registered, it was excluded from our analysis.

We have updated the first sentence of our manuscript’s Results section to clarify that exclusion: “Of 19 RCTs identified in our searches (18 from ClinicalTrials.gov; 1 from personal communication that was excluded due to lack of registration on ClinicalTrials.gov), 8 met final criteria for inclusion in our analysis…”

We have addressed this further in our S3 Appendix: Search Strategy in Detail by adding a new sentence and clarifying that the study excluded for lack of registration was the one identified via personal communication: “Although research community outreach was an important component of our process, our study selection was dictated by the ClinicalTrials.gov searches. Of the 19 studies, we selected 8 after excluding 2 that declined participation or did not respond; 3 with ineligible trial designs including outpatient and prophylaxis studies; 2 with no enrollment; 1 not registered on ClinicalTrials.gov (identified via personal communication); and 3 with sites located outside the US (e.g., RECOVERY and DisCoVeRy, part of SOLIDARITY).”

R3: 4. The authors should perform also Begg and Egger's tests.

Response: We have conducted Begg and Egger’s tests. However, it is generally not recommended to use such tests in meta-analyses with fewer than 10 studies (Sterne et al, 2011; doi:10.1136/bmj.d4002) so we have put the p-values themselves in the supporting information.

We also added results on the estimated between-study standard deviation in treatment effects to facilitate comparison with traditional aggregate-data meta-analyses.

We updated the Results section of the manuscript as follows: “We found no appreciable heterogeneity in estimated treatment-study interactions among the 8 studies after adjusting for individual-level baseline covariates (𝜏 = 0.87 on the log odds scale, 95% CrI, 0.01-5.17); tests for publication bias were inconclusive (S1 Fig).”

We added to the S1 Fig caption the following: “Estimated between-study standard deviations were 1.48 for the “Control” terms (95% CrI 0.16-5.98; on the log-odds scale) and 0.87 for the treatment effects (95% CrI 0.01-5.17). Tests of publication bias applied to unadjusted study-specific proportional ORs from the 6 studies with patients assigned to both HCQ/CQ and control yielded inconclusive results (Egger’s test: p=0.10; Begg’s test: p=0.57); these should be interpreted with caution given the small number of included studies.”

We also renamed the S1 Fig from “Estimated Study Coefficients” to “Assessing Between-Study Heterogeneity.”

R3: The authors should consider to refer to doi: 10.1093/ehjcvp/pvaa105 in the discussion of the manuscript.

Response: Thank you for the suggestion. We reviewed "Management of pregnancy-related hypertensive disorders in patients infected with SARS CoV-2: pharmacological and clinical issues" in consideration for our Discussion but ultimately thought that it was unnecessary to add it to the already extensive section.

Reviewer #4

The authors present a very interesting IPD meta-analysis based on 8 RCTs including unanalyzed data from early trials on the efficacy and safety of HCQ/CQ in COVID-19 prevention and treatment. The paper and analysis are overall of very good quality. I suggested some minors restructuration and additional subgroup analysis that could help strengthen the authors’ claim.

Response: We thank Reviewer #4 for their well-sourced and constructive comments.

R4: Abstract. Authors did not mention what was the primary outcome in the method (they just mentioned the metric of the scale). It is important to know upfront the main measure of their work (at least the conceptual framing behind it). The authors mentioned again the “COVID-19 ordinal scores” in results without giving a specific measure in the abstract. They could conclude on the potential harming effects of HCQ/CQ.

Response: We have clarified in the Abstract that we measured effects using proportional (cumulative) odds ratios by adding the clause: “The primary outcome was a 7-point ordinal scale measured between day 28 and 35 post enrollment; comparisons used proportional odds ratios.”

This is also described in the Methods section.

R4: Introduction. I really appreciate the transparency protocol the authors put into place (though it would be great to have the exact link for Vivli and CCP repositories to be able to consult the database).

Response: The general links to Vivli (https://www.vivli.org) and the Covid Collaboration Platform (http://covidcp.org) are included in the manuscript in the Data collection and harmonization and Trials selection summary sections, respectively. However, the exact links to the actual datasets are part of our Data Availability Statement. That statement will be included in the paper if accepted but is provided as part of the submission form. It reads:

“The ORCHID trial data underlying the results presented in the study are available from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center (https://biolincc.nhlbi.nih.gov/; accession number HLB02372021a). The data for the other studies presented are available from Vivli (https://www.vivli.org): COVID MED, https://doi.org/10.25934/00006535; HAHPS, https://doi.org/10.25934/00006626; NCT04335552, https://doi.org/10.25934/00006861; NCT04344444, https://doi.org/10.25934/00006865; OAHU-COVID19, https://doi.org/10.25934/00006595; TEACH, https://doi.org/10.25934/00006627; and WU352, https://doi.org/10.25934/00006713. Policies for accessing these third-party datasets vary somewhat by study and repository, but requests must be approved and require a signed agreement.”

R4: Outcomes are better explained in the main text; however, authors could justify why they used these specific levels. Was this recommendation base on the studies they re-analyzed? Or is this just a general classification recommended by health authorities and authors had to recode the whole thing? Precisions may help in that part.

Response: We have added the following sentence to the Outcomes subsection of the Methods explaining our choice: “The primary outcome was clinical improvement measured on a 7-point ordinal scale with levels (1) death; […] (7) not hospitalized, without limitations. This scale is relatively coarse compared with others in use (for example the 11-point WHO scale) [34], and was chosen to make the data easier to harmonize.”

R4: Also, authors mentioned a change in the outcome window due to missing data. How did the authors handle missing data in their study?

Response: We describe the handling of missing covariates in the Statistical Analysis section, but have added a clause to clarify how we handled missing outcome data: "Missing baseline covariates were imputed using multiple imputation by chained equations, as implemented in the R package “mice” (version 3.12); missing outcomes were treated as missing at random conditional on the included covariates."

R4: PLOS One is a very generalist journal so it would be great if the authors could recall the strength-weakness of RCTs vs. observational studies for a general audience as well as the benefits of randomization and how it helps to make causal inferences (i.e., controlling for confounding, random sampling criteria, etc. they could cite Schulz et al., 2002 or Vandenbroucke, 2004 for instance). This could also help strengthen the theoretical part of the paper which is unbalanced compared to the other sections of the paper.

Response: We thank Reviewer #4 for their suggestion that we provide more background for a generalist audience. We agree with this suggestion; however, after considering how best to address the issue, we thought it might be even more helpful to include background on our particular study design (individual participant data meta-analysis) and its advantages over individual trials and more traditional meta-analyses of aggregate data (which should be relatively familiar to readers), rather than a high-level discussion of RCTs versus observational studies.

We expanded the Introduction as follows, including adding a citation per your suggestion to Schulz et al, 2002 (#32 below), on external validity:

“The purpose of this study was to ensure utilization of data from unpublished RCTs evaluating HCQ/CQ by combining them with published data, and to synthesize evidence on HCQ/CQ efficacy and safety in hospitalized COVID-19 patients, overall and in subpopulations of interest, by conducting an individual participant data (IPD) meta-analysis. This study design involves pooling subject-level data from multiple studies and possesses many advantages over both individual randomized trials and traditional aggregate-data meta-analyses. Individual trials are usually designed to detect overall effects; the increased sample size in an IPD meta-analysis can enable more precise estimation of subgroup effects [31]. A more diverse sample in a pooled analysis can also improve external validity over individual trials [32]. Compared with aggregate data meta-analyses, IPD meta-analyses are less vulnerable to the ecological fallacy, allow for consistent analytic choices within each study, and enable researchers to consider subgroup effects that were not considered in the original studies [33].”

R4: Results. The authors mentioned early on that, due to missing data, they had to broaden days of post enrollment from 28-30 to 28-35. It would still be interesting though to check whether the main results changed in function of this amendment and how the authors can justify days 35 as the cut-off and not 34 or 33.

Response: The manuscript references sensitivity analyses we conducted using both expanded (day 28-40) and contracted (day 28-30) outcome windows. These did not result in qualitatively different conclusions.

We have added the following paragraph to the Results explaining the choice of day 28-35 window:

“Data on the prespecified primary outcome (the 7-point ordinal scale measured between days 28 and 30) was available for 90% of patients (695 out of 770). In one study, however (TEACH) data was available for only 45% of patients (58 out of 128). Because of this, the decision was made to broaden the primary outcome window to days 28-35. This decision was made without examining the outcome data themselves. With the broader definition, primary outcome data was available for 76% of patients in TEACH (97 out of 128) and 95% of patients overall (734 out of 770).”

We have amended the Outcomes section of the Methods to refer to this:

“We prespecified an outcome window of day 28-30 post-enrollment, which was broadened to day 28-35 after data collection due to missingness (see Results).”

R4: The authors make a compelling case of justifying many sub-group analyses to explore whether the HCQ/CQ presented local benefits for age, gender, BMI, etc. subgroups. Another possible and important subgroup is the timing of the medication. Some authors (see Prodomos & Rumschlag, 2020 or Million et al., 2020) have argued that HCQ is only effective when provided earlier, not associated with worsening disease and safe. Although I’m aware this argument is very borderline, if the authors have enough studies to compared early vs. late treatment, they would be able to respond to those critics and relativize some open-access analysis that claim early treatment showed better improvement.

Response: We have conducted a post-hoc, plug-in subgroup analysis of days between symptom onset and enrollment. We considered adding days between symptom onset and enrollment to our main statistical model, but decided against this on the grounds that it would represent a large departure from pre-registration.

In the supporting information we have added the new S5 Fig and text:

“Post-hoc exploratory analysis of subgroups based on time between symptom onset and enrollment. Subgroups are based on approximate tertiles. Shown are (A) proportional odds ratios from models fit by maximum likelihood within each subgroup, with 95% confidence intervals; (B) the empirical risk of survival for each treatment group within each subgroup, with 95% confidence intervals; and (C) empirical risk differences within each subgroup, with 95% confidence intervals.”

We also made the following changes to the manuscript.

In the Methods section: “We conducted two post-hoc subgroup analyses. The first used quintiles of a baseline risk score given by the expected linear predictor for each study participant under the control condition, as per recommendations from Kent et al [35]. The second was based on the time between symptom onset and enrollment (0-4 days, 5-7 days, ≥8 days; groups based on approximate tertiles).

In the Results section:“There were no substantial effects of HCQ/CQ observed in any prespecified subgroup, nor in a post-hoc subgroup analysis based on the time between symptom onset and enrollment (Fig 2; S5 Fig).”

We have also added this as a post-hoc SAP modification in S3 Table.

R4: Discussion. The discussion part is much more detailed than the introduction. The authors provide a review of RCTs history on HCQ and COVID-19. I would recommend moving some parts of the discussion to the introduction to better contextualize the debate on HCQ and help naive readers understand what’s at stake and why their work is important.

The Annex and online supplementary materials are very complete and detailed.

Response: We considered this suggestion carefully and decided against it for two reasons. First, our discussion of this history of HCQ for COVID-19 is arranged chronologically, and we were concerned that splitting it up might reduce clarity and be harder for readers to follow. Second, our preference is for a simpler Introduction to get readers to our own study’s contributions relatively quickly, with a more in-depth context given in the Discussion after we have presented our main results. However, we welcome the editor’s thoughts and would be happy to reconsider this if he agrees it would improve the manuscript.

Attachment

Submitted filename: DiStefano-HCQResponseR1.pdf

Click here for additional data file.^{(337KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0273526.r003

Decision Letter 1

A M Abd El-Aty

10 Aug 2022

Hydroxychloroquine/Chloroquine for the Treatment of Hospitalized Patients with COVID-19: An Individual Participant Data Meta-Analysis

PONE-D-22-06839R1

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PLoS One. doi: 10.1371/journal.pone.0273526.r004

Acceptance letter

A M Abd El-Aty

18 Aug 2022

PONE-D-22-06839R1

Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis

Dear Dr. Freilich:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. PRISMA-IPD checklist of items to include when reporting a systematic review and meta-analysis of individual participant data (IPD).

(PDF)

Click here for additional data file.^{(215.9KB, pdf)}

S2 Appendix. Methods supplement: Description of the primary outcome model and estimands.

(PDF)

Click here for additional data file.^{(378.6KB, pdf)}

S3 Appendix. Search strategy in detail.

(PDF)

Click here for additional data file.^{(305.1KB, pdf)}

S1 Table. Data dictionary from data harmonization spreadsheet.

(PDF)

Click here for additional data file.^{(193.7KB, pdf)}

S2 Table. Risk of bias assessment.

(PDF)

Click here for additional data file.^{(163.3KB, pdf)}

S3 Table. Changes to the prespecified statistical analysis plan.

(PDF)

Click here for additional data file.^{(114.3KB, pdf)}

S4 Table. Primary, secondary, and safety outcomes.

(PDF)

Click here for additional data file.^{(193.4KB, pdf)}

S5 Table. Trial characteristics: Treatment groups, participant assessment, and inclusion/exclusion criteria.

(PDF)

Click here for additional data file.^{(167.1KB, pdf)}

S6 Table. Merging trial arms.

(PDF)

Click here for additional data file.^{(168.6KB, pdf)}

S1 Fig. Assessing between-study heterogeneity.

(PDF)

Click here for additional data file.^{(221.3KB, pdf)}

S2 Fig. Conditional covariate effects.

(PDF)

Click here for additional data file.^{(1.3MB, pdf)}

S3 Fig. Estimated mortality rate in subgroups under both control and HCQ/CQ.

(PDF)

Click here for additional data file.^{(624.7KB, pdf)}

S4 Fig. Posterior predictive check of primary outcomes by study.

(PDF)

Click here for additional data file.^{(573.8KB, pdf)}

S5 Fig. Exploratory analysis of time between symptom onset and enrollment.

(PDF)

Click here for additional data file.^{(176.8KB, pdf)}

Attachment

Submitted filename: Decision letterr.docx

Click here for additional data file.^{(14.4KB, docx)}

Attachment

Submitted filename: DiStefano-HCQResponseR1.pdf

Click here for additional data file.^{(337KB, pdf)}

Data Availability Statement

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PERMALINK

Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis

Leon Di Stefano

Elizabeth L Ogburn

Malathi Ram

Daniel O Scharfstein

Tianjing Li

Preeti Khanal

Sheriza N Baksh

Nichol McBee

Joshua Gruber

Marianne R Gildea

Megan R Clark

Neil A Goldenberg

Yussef Bennani

Samuel M Brown

Whitney R Buckel

Meredith E Clement

Mark J Mulligan

Jane A O’Halloran

Adriana M Rauseo

Wesley H Self

Matthew W Semler

Todd Seto

Jason E Stout

Robert J Ulrich

Jennifer Victory

Barbara E Bierer

Daniel F Hanley

Daniel Freilich

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Trials selection summary

Data collection and harmonization

Outcomes

Baseline and post-baseline variables

Statistical analysis

Risk of bias assessment

Registration

Results

Study characteristics

Fig 1. Trial selection process.

Risk of bias assessment

Patient characteristics

Table 1. Participant characteristics overall and in each trial.

Primary outcome: Pooled and subgroup analysis

Fig 2. Primary outcome data by treatment group.

Table 2. Primary, secondary, and safety outcomes, overall and by trial.

Fig 3. Subgroup analysis of differences in ordinal scale at days 28–35.

Mortality: Pooled and subgroup analysis

Fig 4. Subgroup analysis of differences in mortality at days 28–35.

Sensitivity analyses

Exploratory analysis of secondary and safety outcomes

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

A M Abd El-Aty

Roles

Author response to Decision Letter 0

Decision Letter 1

A M Abd El-Aty

Roles

Acceptance letter

A M Abd El-Aty

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS