Abstract
Background
Attrition is common among supportive/palliative oncology clinical trials. Few studies have documented the reasons and predictors for dropout. We aimed to determine the rate, reasons and factors associated with attrition both before reaching the primary endpoint and the end of study.
Methods
We conducted a review of all prospective interventional supportive/palliative oncology trials in the Department of Palliative Care and Rehabilitation Medicine at MD Anderson Cancer Center between 1999–2011. Patient and study characteristics and attrition data were extracted.
Results
1214 patients were included in 18 clinical trials. The median age was 60, 41% had performance status ≥3, median fatigue 7/10 and median dyspnea 2/10. The attrition rate was 26% (95% confidence interval [CI] 23%-28%) for the primary endpoint and 44% (95% CI 41%-47%) for the end of study. Common reasons for primary endpoint dropout were symptom burden (21%), patient preference (15%), hospitalization (10%) and death (6%). Primary endpoint attrition was associated with higher baseline intensity of fatigue (odds ratio [OR]=1.10 per point, P=0.01) and longer study duration (P=0.04). End of study attrition was associated with higher baseline levels of dyspnea (OR=1.06, P=0.01), fatigue (OR=1.08, P=0.01), Hispanic race (OR=1.87, P=0.002), higher education (P=0.02), longer study duration (P=0.01) and outpatient studies (P=0.05).
Conclusions
The attrition rate was high in supportive/palliative oncology clinical trials, and was associated with various patient characteristics and high baseline symptom burden. These findings have implications for future clinical trial design including eligibility criteria and sample size calculation.
Keywords: Attrition, Clinical trial, Neoplasms, Palliative Care, Supportive Care, Predictors, Research Design, Symptoms
INTRODUCTION
Over the past few decades, palliative oncology has evolved into a discipline that places an increased emphasis on evidence-based practice. This is supported by a growing number of clinical trials.1, 2 However, there remains many challenges to conducting palliative cancer care research, including limited research funding, difficulty in recruiting and retaining patients, few trained personnel, and other methodological issues.3–6
Because of the frail population in palliative oncology clinical studies, patient dropout is an important consideration. Indeed, attrition is a common concern among palliative care clinical trials,7, 8 and contributes to selection bias, an under-powered study, and pre-mature trial closure. Yet, there are only few studies documenting the pattern of and reasons for dropout in palliative care clinical trials.9 A better understanding of the rate of dropout in palliative oncology clinical studies could help investigators to plan the appropriate sample size for grant budget and analysis purposes. Furthermore, identification of the reasons and predictive factors contributing to attrition may allow us to better design studies to improve retention, thus maximizing the number of patients with the outcome of interest assessed. In this study, we determined the rate, reasons and factors associated with attrition both before reaching the primary endpoint and the end of study.
PATIENTS AND METHODS
Subjects
All 18 prospective interventional clinical trials involving advanced cancer patients in the Department of Palliative Care and Rehabilitation Medicine at MD Anderson Cancer Center, Houston, Texas between 1999–2011 were included. All studies were closed to patient entry. Because this was a secondary data analysis, patients were not re-contacted or re-consented. The Institutional Review Board at MD Anderson Cancer Center approved this study and waived the requirement for informed consent.
Data Collection on Clinical Trial Characteristics
We retrieved information on various clinical trial characteristics from study protocols and published articles if available. These included the study objective (i.e. symptom of interest), timing of primary outcome assessment, study design (i.e. randomization, blinding, active intervention, and control intervention), study duration, study setting (inpatient or outpatient), number of study sites, funding, planned sample size, number of patients enrolled, number of dropouts, and the final sample size. Inpatient studies were those that involved hospitalized patients, whereas outpatient studies included those seen at our ambulatory clinic or home hospice. To maximize generalizability of study findings, clinical trials by our group and many others generally include all histologies rather than specific cancer types. Dropouts due to symptom progression or death were not considered as protocol failures in our clinical trials.
Data Collection for Patient Characteristics
We collected information on various patient characteristics at the time of clinical trial enrollment, including age, sex, race, marital status, education, cancer diagnosis, and Eastern Cooperative Oncology Group (ECOG) performance status, and the Edmonton Symptom Assessment Scale (ESAS). ESAS is a validated scale designed to assess 10 common symptoms (i.e. pain, fatigue, nausea, depression, anxiety, drowsiness, shortness of breath, appetite, feelings of well-being and sleep). Patients were asked to rate the severity of their symptoms over the previous 24 hours using a numerical rating scale of 0–10, with 0 meaning that the symptom is absent and 10 meaning the worst possible symptom.10–12 Although a vast majority of studies incorporated ESAS and some also had ECOG performance status, they were not uniformly included in all studies.
All patients were classified based on whether they completed the primary outcome measure and whether they completed the entire study, and the reasons for dropout where available.
Statistical Analysis
We summarized the baseline patient demographics and study characteristics using descriptive statistics, including medians, means, standard deviations, ranges, interquartile ranges and frequencies.
We calculated the two study level outcomes, attrition rate prior to the primary endpoint and prior to the end of study, by dividing the number of patients who dropped out before the specific time point by the total number of patients enrolled in each clinical trial. Study level predictors for the two outcomes were then analyzed using the Spearman correlation test for continuous variables, and the Kruskal-Wallis test for categorical variables.
We determined patient level predictors of dropout prior to the primary endpoint by single predictor random effects generalized linear mixed model (GLMM), with dropout as the dichotomous outcome, predictors as fixed effects, and study as a random effect to account for differences among clinical trials. Because a number of patient characteristics were not collected across all studies (e.g. performance status), we applied multiple imputation techniques to the patient level dataset to produce estimates for the missing data. Overall, 19% (range 0%-59%) of the data were missing requiring imputation. Variables that were significant at the 0.10 level in single predictor GLMM were then included in the multiple predictor GLMM. P-values of less than 0.05 were considered to be statistically significant. A similar analysis was conducted for attrition prior to the end of study.
We used IVEware2 (University of Michigan, Michigan, build 2012.02) to perform the imputation using linear regression techniques. For all analyses other than imputations, we used SAS/STAT version 9.2 software (SAS Inc., Cary NC).
RESULTS
Clinical Trial Characteristics
Table 1 summarizes the 18 interventional clinical trials. 15/18 (83%) were randomized controlled trials, and 10/18 (56%) included placebo as control. Cancer-related fatigue was the most common symptom being investigated. Only 2 of the studies enrolled the planned number of subjects without dropouts.
Table 1.
Clinical Trial Characteristics (N=18)
| Study Name | Primary symptom | Randomized controlled trial | Placebo trial | Multicenter study | Outpatient study | Days between enrollment and primary endpoint | Days between enrollment and end of study | External Funding | Total enrolled |
|---|---|---|---|---|---|---|---|---|---|
| ID00-031 Dexamethasone27 | Nausea | Yes | Yes | Yes | Yes | 8 | 8 | Yes | 51 |
| ID00-021 Morphine/methadone28 | Pain | Yes | No | Yes | Yes | 39 | 29 | Yes | 103 |
| ID00-030 Nebulized/subcutaneous morphine29 | Dyspnea | Yes | No | Yes | No | 1 hour | 2 | No | 12 |
| ID01-166 Methylphenidate26 | Fatigue | No | No | No | Yes | 8 | 28 | No | 31 |
| ID02-626 Donepezil25 | Fatigue | No | No | No | Yes | 8 | 8 | Yes | 27 |
| ID02-032 Hydration/placebo30 | Dehydration | Yes | Yes | Yes | No | 2 | 2 | Yes | 51 |
| 2003-0425 Donepezil/placebo25 | Fatigue | Yes | Yes | No | Yes | 8 | 15 | Yes | 143 |
| 2003-0537 Methylphenidate/placebo31 | Fatigue | Yes | Yes | No | Yes | 8 | 36 | No | 112 |
| 2005-0613 Methylphenidate/placebo | Fatigue | Yes | Yes | No | Yes | 15 | 36 | Yes | 193 |
| 2005-0816 Dexamethasone/placebo32 | Fatigue | Yes | Yes | Yes | Yes | 15 | 43 | Yes | 126 |
| 2005-0901 Melatonin/placebo | Anorexia | Yes | Yes | No | Yes | 29 | 43 | No | 74 |
| 2005-0916 Mirtazapine/placebo | Anorexia | Yes | Yes | No | Yes | 15 | 29 | No | 46 |
| 2005-0980 Thalidomide/placebo | Cachexia | Yes | Yes | No | Yes | 15 | 28 | No | 32 |
| 2006-0494 Hydration/placebo | Dehydration | Yes | Yes | Yes | Yes | 4 | 15 | Yes | 131 |
| 2006-0591 Interactive voice response | Symptoms | Yes | No | No | Yes | 15 | 15 | No | 33 |
| 2006-0641 Methadone as co-opioid | Pain | Yes | No | No | Yes | 15 | 15 | No | 5 |
| 2006-0739 Multimodal interventions | Cachexia | No | No | No | Yes | 29 | 29 | No | 8 |
| 2007-0477 Morphine/methadone | Pain | Yes | No | No | Yes | 29 | 84 | Yes | 36 |
Patient Characteristics
The baseline characteristics of the 1214 cancer patients at enrollment are shown in Table 2. Gastrointestinal, respiratory and breast were the most common cancer sites. One third of patients had an ECOG performance status of 3 or more. The median symptom burden by ESAS was relatively high, particularly for fatigue, anorexia and sleep.
Table 2.
Patient Characteristics (N=1214)
| Characteristics | N (%)1 |
|---|---|
| Average age (range) | 60 (23–93) |
| Female sex | 685 (56) |
| Race | |
| White | 691 (57) |
| Black | 153 (13) |
| Hispanic | 129 (11) |
| Asian | 23 (2) |
| Not available | 218 (18) |
| Married | |
| Yes | 619 (51) |
| No | 312 (26) |
| Not available | 283 (23) |
| Education | |
| High school or less | 217 (18) |
| College | 237 (20) |
| Advanced degree | 39 (3) |
| Not available | 721 (59) |
| ECOG performance status | |
| 0 | 20 (2) |
| 1 | 184 (15) |
| 2 | 251 (21) |
| 3 | 171 (14) |
| 4 | 57 (5) |
| Not available | 531 (44) |
| Cancer | |
| Breast | 210 (17) |
| Gastrointestinal | 282 (23) |
| Genitourinary | 112 (9) |
| Gynecological | 93 (8) |
| Head and neck | 63 (5) |
| Hematological | 58 (5) |
| Other | 144 (12) |
| Respiratory | 222 (18) |
| Median Edmonton Symptom Assessment Scale (interquartile range) | |
| Pain | 4 (2–7) |
| Fatigue | 7 (5–8) |
| Nausea | 1 (0–4) |
| Depression | 2 (0–5) |
| Anxiety | 2 (0–5) |
| Drowsiness | 4 (2–6) |
| Appetite | 5 (3–8) |
| Wellbeing | 5 (3–7) |
| Dyspnea | 2 (0–5) |
| Sleep | 5 (2–7) |
| Attrition rates | |
| Prior to the primary endpoint | 311 (26 |
| Prior to the end of study | 535 (44) |
Unless otherwise specified
Rates and Reasons for Attrition
At the clinical trial level, the median attrition rate was 28% (interquartile range (IQR) 16%-38%) for the primary endpoint, and 44% (IQR 28%-58%) for the end-of-study.
At the patient level, the attrition rate was 26% (95% confidence interval 23%-28%) for the primary endpoint and 44% (95% confidence interval 41%-47%) for the end-of-study.
The main reasons for attrition were patient withdrawal and clinical deterioration, accounting for 48%-52% and 23%-35% of the dropouts, respectively (Table 3). Specifically, a high symptom burden, which may or may not be related to the clinical trial intervention, was the most common reason for patient withdrawal.
Table 3.
Reasons for Dropout
| Reasons | Dropout before primary endpoint N=311 (%) | Dropout before study completion N=535 (%) |
|---|---|---|
| Lost to follow up | 14 (5) | 22 (4) |
| Deterioration | ||
| Death | 19 (6) | 45 (8) |
| Disease progression | 3 (1) | 5 (1) |
| Altered mental status | 12 (4) | 17 (3) |
| Unable to take medication orally | 5 (2) | 5 (1) |
| Transfer to Hospice | 5 (2) | 12 (2) |
| Hospital admission | 30 (10) | 43 (8) |
| Patient withdrawal | ||
| Patient decision | 47 (15) | 93 (17) |
| Symptom burden | 65 (21) | 87 (16) |
| Family/caregiver decision | 10 (3) | 18 (3) |
| Other clinical trial/therapy | 8 (3) | 11 (2) |
| Logistical | 1 (0) | 1 (0) |
| Reason unknown | 30 (10) | 55 (10) |
| Study protocol | ||
| Physician decision/adverse event | 7 (2) | 9 (2) |
| Study violation | 8 (3) | 11 (2) |
| Non compliance | 16 (5) | 24 (4) |
| Not documented | 28 (9) | 77 (14) |
Patient Characteristics associated with Attrition
Table 4 shows our univariate and multivariate analyses to identify factors predictive of attrition. We found that higher intensity of fatigue was associated with higher rates of dropouts prior to the primary endpoint. Our study also revealed that Hispanic race, higher education, non-Christians, and higher intensity of dyspnea and fatigue were associated with dropout prior to the end of study. When we repeated the above analyses but without the 2 inpatient studies, the findings were similar, with the exception that advanced education was also associated with attrition prior to the primary endpoint.
Table 4.
Patient Factors Associated with Attrition Prior to Primary Endpoint and Prior to End of Study
| Characteristics | Attrition Prior to Primary Endpoint | Attrition Prior to End of Study | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Dropped Out N (%)1 | Completed N (%)1 | Univariate P-Value2 | Odds ratio (95% CI)3 | Multivariate P-Value3 | Dropped Out (N%)1 | Completed N (%)1 | Univariate P-Value2 | Odds ratio (95% CI)3 | Multivariate P-Value3 | |
| Age | 1214 | 0.06 | 1.01 (1.00, 1.02) | 0.10 | 1214 | 0.1 | 1.01 (1.00, 1.02) | 0.11 | ||
| Female sex | 178 (57) | 507 (56) | 0.38 | 291 (54) | 394 (58) | 0.45 | ||||
| Race | 0.17 | 0.008 | ||||||||
| Asian | 20 (6) | 49 (5) | 28 (5) | 41 (6) | 1.74 (0.96, 3.16) | 0.07 | ||||
| Black | 51 (16) | 150 (17) | 81 (15) | 120 (18) | 0.85 (0.59, 1.23) | 0.39 | ||||
| Hispanic | 52 (17) | 108 (12) | 87 (16) | 73 (11) | 1.87 (1.26, 2.76) | 0.002 | ||||
| White | 188 (60) | 596 (66) | 339 (63) | 445 (66) | 1 | REF | ||||
| Married | 190 (61) | 577 (64) | 0.63 | 331 (62) | 436 (64) | 0.82 | ||||
| Education | 0.10 | 0.05 | ||||||||
| High school or less | 163 (52) | 461 (51) | 263 (49) | 361 (53) | 0.67 (0.46, 0.97) | 0.04 | ||||
| College | 84 (27) | 319 (35) | 170 (32) | 233 (34) | 0.64 (0.43, 0.95) | 0.03 | ||||
| Advanced degree | 64 (21) | 123 (14) | 102 (19) | 85 (13) | 1 | REF | ||||
| Cancer diagnosis | 311 (100) | 903 (100) | 0.63 | 535 (100) | 679 (100) | 0.62 | ||||
| ECOG PS | 0.19 | 0.045 | ||||||||
| 0–1 | 128 (41) | 378 (42) | 211 (39) | 295 (43) | 0.79 (0.56, 1.10) | 0.16 | ||||
| 2 | 68 (22) | 224 (25) | 118 (22) | 174 (26) | 0.75 (0.51, 1.10) | 0.15 | ||||
| 3–4 | 115 (37) | 301 (33) | 206 (39) | 210 (31) | 1 | REF | ||||
| ESAS Anxiety | 1214 | 0.90 | 1214 | 0.53 | ||||||
| ESAS Appetite | 1214 | 0.01 | 1.04 (0.99, 1.09) | 0.11 | 1214 | 0.03 | 1.02 (0.97, 1.07) | 0.38 | ||
| ESAS Depression | 1214 | 0.78 | 1214 | 0.69 | ||||||
| ESAS Drowsiness | 1214 | 0.65 | 1214 | 0.69 | ||||||
| ESAS Fatigue | 1214 | 0.0001 | 1.10 (1.03, 1.17) | 0.01 | 1214 | 0.0009 | 1.08 (1.02, 1.15) | 0.01 | ||
| ESAS Nausea | 1214 | 0.32 | 1214 | 0.10 | ||||||
| ESAS Pain | 1214 | 0.04 | 1.03 (0.98, 1.09) | 0.26 | 1214 | 0.06 | 1.01 (0.96, 1.07) | 0.59 | ||
| ESAS Sleep | 1214 | 0.21 | 1214 | 0.09 | 1.02 (0.98, 1.07) | 0.31 | ||||
| ESAS dyspnea | 1214 | 0.002 | 1.05 (1.00, 1.10) | 0.058 | 1214 | 0.0002 | 1.06 (1.01, 1.11) | 0.009 | ||
| ESAS Wellbeing | 1214 | 0.12 | 1214 | 0.09 | 0.99 (0.93, 1.04) | 0.61 | ||||
Abbreviations: CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group Performance Status; ESAS, Edmonton Symptom Assessment Scale; MDAS, Memorial delirium assessment scale; REF, reference
For categorical variables. All variables had 1214 patients because imputation has filled in the missing data
Univariate random effects generalized linear mixed model
Multivariate random effects generalized linear mixed model
Clinical Trial Characteristics associated with Attrition
At the study level, we found longer study duration was associated with attrition prior to the primary endpoint (Spearman correlation 0.49, P=0.04) and the end of study (Spearman correlation 0.59, P=0.01). Outpatient studies (47% vs. 6% for inpatient studies, P=0.05) were also more likely to have dropouts prior to the end of study (Table 5).
Table 5.
Study Factors Associated with Attrition Prior to Primary Endpoint and Prior to End of Study
| Characteristics | Prior to Primary Endpoint | Prior to End of Study | ||||
|---|---|---|---|---|---|---|
| Median attrition % (95% CI) when variable present | Median attrition % (95% CI) when variable absent | P-value1 | Median attrition % (95% CI) when variable present | Median attrition % (95% CI) when variable absent | P-value1 | |
| Anorexia or cachexia clinical trial | 39 (34, 41) | 26 (3, 63) | 0.14 | 53 (46, 74) | 35.7 (3, 86) | 0.21 |
| External funding | 26 (4, 56) | 34 (3, 63) | 0.51 | 36 (4, 86) | 46 (3, 75) | 0.76 |
| Fatigue clinical trial | 38 (3, 50) | 50 (4, 86) | 0.08 | 38 (3, 50) | 50 (4, 86) | 0.31 |
| Multi-center study | 31 (3, 63) | 6 (4, 8) | 0.19 | 25 (4, 69) | 48 (3, 86) | 0.19 |
| Outpatient study | 31 (3, 63) | 6 (4, 8) | 0.07 | 48 (3, 86) | 6 (4, 8) | 0.049 |
| Randomized controlled trial | 30 (4, 60) | 26 (3, 63) | 0.77 | 46 (4, 86) | 26 (3, 75) | 0.52 |
Abbreviations: CI, confidence interval
Kruskal-Wallis Test
DISCUSSION
Even though all 18 clinical trials included in this study were designed and conducted by an experienced team of researchers and the study criteria were devised to minimize attrition, 1 in 4 patients dropped out prior to the primary endpoint, and 1 in 2 patients dropped out prior to the end-of-study. The attrition rate varied widely among studies. Hispanic race, higher education, non-Christians and a high symptom burden at the time of enrollment were associated with a higher risk of dropout. Our findings have implications for future study design, including sample size estimation and measures to minimize dropouts.
To our knowledge, this is the most comprehensive study examining the issue of attrition in an aggregate of symptom control clinical trials. The rate of attrition in this study is generally consistent with the literature. Oldervoll et al. reported that 46% of the cancer patients dropped out in a Phase II feasibility study of exercise in the palliative setting.13 In a National Cancer Institute funded study, McMillan et al. found that only 38% of subjects had complete data at followup.14 A study of 40 randomized controlled trials on cognitive behavioral interventions for pediatric chronic pain found a mean attrition rate of 20% (range 0–54%) for initial followup and 32% (range 0–59%) for extended followup.15 Interestingly, the rate of attrition in our symptom control trials is also comparable to that of other palliative care studies on health services research. A review of methodological issues in effectiveness research on palliative cancer care found high dropout rates in the clinical trials, ranging between 34% and 80%.7 More recently, Zimmermann et al. reported in a systematic review evaluating the effectiveness of specialist palliative care that the median rate of loss to follow up for quality of life and satisfaction was 40% (range 3%-92%) among 20 studies.16
The major reasons for attrition were patient deterioration and patient decision to withdraw from study, accounting for approximately 80% of the dropouts. This is not surprising given that patients enrolled onto palliative care clinical trials generally have a poor performance status and short life-expectancy. Many acute complications occur in the last few weeks/months of life, resulting in acute deterioration, worsening distress, hospitalizations and death, affecting patients’ ability and willingness to continue with the study.17 Importantly, others have also reported that death, physical decline, and emotional distress were major contributors to dropout.9, 14, 18 We found that one of the key reasons for patients to withdraw is increasing symptom distress. Further studies are required to determine whether this is because of the natural progression of disease, adverse effect related to study intervention, or the inability of the study intervention to control the target symptom. Interestingly, few patients dropped out because of safety concerns, protocol violation or lost to followup. This may be explained by the attention paid to study design and careful followup by our research team.
As shown in this study, attrition can have a major impact on the quality of the study. McWhinney et al. initiated a randomized controlled trial to evaluate a palliative care home support team, but had to terminate the study early due to 36% attrition.9 Identification of risk factors for dropout may allow us to design studies with minimal attrition. We found that Hispanic race, non-Christian religious affiliation and higher education were associated with higher dropouts in multivariate analysis. Others have also identified minorities as a contributor to attrition.19, 20 This may be related to their distrust of the medical system, language barrier, and the lack of resources (e.g. transportation to hospital), making it less appealing more for them to stay on study.21–23 Further research is needed to determine why patients with lower levels of education were more likely to stay on the clinical trials in our study. Furthermore, we found that high levels of fatigue, dyspnea and poor performance status were predictors of attrition. This is ironic given that the primary concern for these clinical trials, namely symptom burden and function, are the very contributors to attrition. This highlights the unique challenges in conducting clinical trials in palliative care, particularly for dyspnea and fatigue studies. We also found that inpatient studies had the best overall retention rates, which may be explained by the short duration and direct supervision of care of these trials.
How can we minimize dropouts from symptom research clinical trials? We recommend that all research protocols should keep the study as short as possible, minimize the study burden, and incorporate close monitoring and support for the patient. This is particularly important for fatigue and dyspnea studies. For explanatory trials, investigators may choose to enroll subjects most likely to complete the study, such as hospitalized patients, non-minorities, those with a longer life expectancy and better performance status.24 Moreover, we need to recognize that high attrition may be inevitable in symptom research clinical trials, particularly Phase III trials of pragmatic nature, research on dyspnea and fatigue, and studies involving minorities and end-of-life (i.e. weeks to days of survival) populations. Findings from symptom control trials conducted in patients with early cancer may not be generalizable to the patients with advanced cancer because of their unique needs. Thus, it is imperative to conduct studies involving patients who are frail and symptomatic, despite a higher expected attrition rate. Funding proposals and research protocols should plan to enroll a higher number of patients such that an adequate sample size can be achieved for the primary outcome.
Only 2 of 18 studies enrolled the planned number of subjects.25, 26 The small sample size precludes detail statistical analysis. Funding, study design and committed research staff are likely important contributors to successful enrollment. Further research is needed to identify factors associated with study completion.
This study has several limitations. First, the clinical trials were all conducted by a single research group at a tertiary care cancer center. Our findings may not be generalized to other settings. Second, although data were collected prospectively, some variables such as performance status and education were not routinely collected in all clinical trials. Imputation techniques were used to maximize the data available for analysis. Future studies should routinely collect symptom batteries, performance status and cognitive status to facilitate interpretation of findings and comparison of patient populations. Third, we did not include a number of variables, such as patient interest, history of participation and travel distance, which could potentially account for attrition. Fourth, the number of clinical trials was small, and thus did not allow for a detail analysis of study-related factors associated with attrition. Further studies are needed to examine this issue in more detail. Finally, patient deterioration leading to dropout may be related to cancer progression, treatment adverse effects and/or failure of supportive therapies to control symptoms, and it is often difficult to distinguish between these possibilities.
In summary, attrition was high among our cohort of symptom control clinical trials. Patient deterioration and withdrawal were the major reasons for dropouts. We identified various patient characteristics, poor performance status and high symptom burden as predictors of attrition. To safeguard the scientific integrity of palliative care clinical trials, investigators need to routinely anticipate attrition and incorporate various measures to minimize dropouts during trial design.
Acknowledgments
Funding: Supported in part by National Institutes of Health grants RO1NR010162-01A1, RO1CA122292-01, and RO1CA124481-01 (EB). This study is also supported by the MD Anderson Cancer Center Support Grant (CA 016672) and an institutional startup grant #18075582 (DH). The sponsor of the study had no role in study design, data collection, analysis, interpretation, or writing of the report.
Footnotes
Financial disclosures: the authors have no financial disclosures
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