Publication bias in pharmacogenetics of adverse reaction to antiseizure drugs: An umbrella review and a meta-epidemiological study

S Bally; J Cottin; M C Gagnieu; J C Lega; C Verstuyft; S Rheims; G Lesca; M Cucherat; Guillaume Grenet

doi:10.1371/journal.pone.0278839

. 2022 Dec 30;17(12):e0278839. doi: 10.1371/journal.pone.0278839

Publication bias in pharmacogenetics of adverse reaction to antiseizure drugs: An umbrella review and a meta-epidemiological study

S Bally ¹, J Cottin ², M C Gagnieu ³, J C Lega ^1,⁴, C Verstuyft ^5,⁶, S Rheims ⁷, G Lesca ⁸, M Cucherat ^1,², Guillaume Grenet ^2,^*

Editor: Huijuan Cao⁹

¹Laboratoire de Biométrie et Biologie Evolutive UMR5558, Université Lyon 1, CNRS, Villeurbanne, France

²Service Hospitalo-Universitaire de Pharmacotoxicologie, Pôle de Santé Publique, Hospices Civils de Lyon, Lyon, France

³Laboratoire de Pharmacologie, Groupement Hospitalier Sud, Hospices Civils De Lyon, Lyon, France

⁴Service de Médecine Interne et Vasculaire, Hôpital Lyon Sud, Hospices Civils de Lyon, Lyon, France

⁵CESP, MOODS Team, INSERM, Faculté de Médecine, Université Paris-Saclay, Le Kremlin Bicêtre, France

⁶Service de Génétique Moléculaire, Pharmacogénétique et Hormonologie de Bicêtre, Hôpitaux Universitaires Paris-Sud, Assistance Publique-Hôpitaux de Paris, Hôpital de Bicêtre, Le Kremlin Bicêtre, France

⁷Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon 1 University, Lyon, France

⁸Service de Génétique, Groupement Hospitalier Est, Hospices Civils De Lyon, Université Lyon 1, Lyon, France

⁹Beijing University of Chinese Medicine, CHINA

Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: SB, JC, MGC, GG declare that they have no competing interest. JCL has received speaker fees and honoraria from Roche. CV has received consulting fees from Genzyme, Novartis and speaker honoraria from Galapagos. SR received speaker and/or consulting fees from UCB Pharma, EISAI, GW Pharma, Idorsia, LivaNova, and Arvelle Therapeutics. GL has received speaker honoraria from GWpharma, Eisai and Biomarin. MC has received consulting fees from Boehringer Ingelheim, SANOFI, AstraZeneca, EISAI and speaker honoraria from SANOFI. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

^✉

* E-mail: guillaume.grenet@chu-lyon.fr

Roles

S Bally: Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft

J Cottin: Investigation, Writing – review & editing

M C Gagnieu: Writing – review & editing

J C Lega: Writing – review & editing

C Verstuyft: Writing – review & editing

S Rheims: Writing – review & editing

G Lesca: Writing – review & editing

M Cucherat: Conceptualization, Methodology, Supervision, Writing – review & editing

Guillaume Grenet: Conceptualization, Investigation, Methodology, Supervision, Writing – original draft

Huijuan Cao: Editor

PMCID: PMC9803138 PMID: 36584134

Abstract

Publication bias may lead to a misestimation in the association between pharmacogenetic biomarkers (PGx) and antiseizure drug’s adverse effects (AEs). We aimed to assess its prevalence in this field. We searched for systematic reviews assessing PGx of antiseizure drug’s AEs. For each unique association between a PGx, a drug and its AE, we used the available odds ratio (ORs) to generate corresponding funnel plots. We estimated the prevalence of publication bias using visual inspections and asymmetry tests. We explored the impact of publication bias using ORs adjusted for potential publication bias. Twenty-two associations were available. Our visual analysis suggested a publication bias in five out twenty-two funnel plots (23% [95%CI: 8; 45]). The Egger’s test showed a significant publication bias in one (HLA-B*15:02 and phenytoin-induced Stevens-Johnson syndrome or toxic epidermal necrolysis, p = 0.03) out of nine (11% [95%CI: 0; 48]) and the Begg’s test in one (HLA-B*15:02 and carbamazepine-induced serious cutaneous reactions, p = 0.02) out of ten (10% [95%CI: 0; 45]) assessable funnel plots. Adjusting for publication bias may reduce by half the ORs of the pharmacogenetics associations. Publication bias in the pharmacogenetic of antiseizure drug’s AEs is not uncommon and may affect the estimation of the effect of such biomarkers. When conducting pharmacogenetic studies, it is critical to publish also the negative one.

Introduction

Rationale

Systematic reviews and meta-analyses help synthesize the estimates from several clinical studies. However, their results may be affected by publication bias [1]. At the beginning, publication bias has been pointed out regarding the risk of treatment efficacy overestimation [2]. Then, publication bias has also been identified for assessing the risk of adverse drug reactions (ADRs) [3]. Furthermore, publication bias has been documented in genetic epidemiology in general [4]. Previous meta-epidemiological studies assessed publication bias in various areas [5–8]. However, the extent of publication bias in pharmacogenetics remains unclear.

Several genetic variants have been associated with an increased risk of antiseizure drug’s adverse effects (AEs) [9]. Some of them seem to be reliable, as HLA-B and carbamazepine/phenytoin-induced severe cutaneous reactions, allowing clinical implementation of pharmacogenetic results [10, 11]. Others showed some discrepancies, such as for lamotrigine and the risk of Stevens-Johnson syndrome (SJS) or toxic epidermal necrolysis (TEN) [10]. Such pharmacogenetic associations may also vary across different populations [12]. A publication bias has been documented regarding the assessment of antiseizure drugs, such as topiramate [13] or pregabalin for example [14]. Previous meta-analyses assessing pharmacogenetic risk factors of antiseizure drug’s AE suggested a potential publication bias, including HLA-B*15:02 polymorphism [15]. Other meta-analyses did not report a publication bias, but the number of available trials was often low, ranging from two to eleven studies in three meta-analysis [16–18]. Thus, the statistical power for detecting funnel plot asymmetry was probably insufficient [19]. The prevalence and the potential impact of publication bias in the pharmacogenetics of antiseizure drug’s AEs remain unclear.

Objectives

Our hypothesis was that the publication bias is particularly sizable in the pharmacogenetics of antiseizure druginduced adverse reactions. We aimed to assess its prevalence in this area first. Then, we aimed to illustrate its potential impact on the estimation of those pharmacogenetics biomarkers.

Methods

Protocol

We conducted a meta-epidemiological study. We did not register the protocol, but we formulated the hypothesis a priori. Methods and results were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for systematic review [20] and the dedicated guideline for reporting meta-epidemiological methodology research [21]. We conducted an umbrella review to identify systematic reviews and meta-analyses of interest. We did not search directly for clinical studies themselves. Indeed, the assessment of publication bias requires as many clinical studies as possible, and is expected to be inconclusive in case of isolated (i.e non-replicated) clinical studies. Therefore, we opted for an umbrella review of published systematic reviews. One bibliographic reference of a systematic review could include several published meta-analyses (pMA), one for each unique triplet association _{[genotype-drug-ADR]}. Each unique triplet association _{[genotype-drug-ADR]} may be studied across different published meta-analyses. We gathered all the informative clinical studies assessing the same triplet association _{[genotype-drug-ADR]} reported across the published meta-analyses from the different systematic reviews. Combining the data from previous overlapping published meta-analyses allowed increasing sample size of data given the number of clinical studies. We then assessed the presence of a publication bias and its impact for each unique triplet association _{[genotype-drug-ADR]}.

Eligibility criteria

Systematic reviews were included if they met the following inclusion criteria: (i) meta-analyses of clinical studies (observational or comparative trials) addressing antiseizure drugs and (ii) reporting of ADR related to pharmacogenetic biomarkers.

Information sources

We conducted an umbrella review on PubMed, up to January 29, 2019, seeking published meta-analyses investigating the association between a genetic variant and an adverse reaction in patients treated with antiseizure drugs. We limited the information source to the Medline database. Indeed, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for HLA genotype and Use of Carbamazepine and Oxcarbazepine was restricted to the use of “the PubMed database” [11]. Using the same restriction allowed to estimate the impact of publication bias in the context of the current practice of guidelines elaboration in this area.

Search strategy

The Medline database was searched using the following keywords: “pharmacogen*” OR “genetic variant” OR “polymorphism” OR “allele” OR “association” AND “epilepsy” OR “antiepileptics” OR “anticonvulsants” AND “adverse event” OR “toxicity” OR “serious event” OR “meta-analysis” OR “systematic review” (see the search strategy in Supplementary information), without restrictions regarding the year of publication.

Study selection and data extraction

Abstracts of bibliographic references were screened based on their title and abstract, and then selected using their full text, reason for exclusion being tracked. We extracted descriptive characteristics: authors, publication date, type of study, and ethnicity. We extracted study design, pharmacogenetic associations as reported by the authors (name of the genetic variant, antiseizure drug assessed, control treatment used, and adverse reactions). Indeed, as we used previously published systematic reviews of clinical studies, our extraction is limited by the author’s definition of the genetic variant (i.e not the rs number if not reported) and of the adverse drug reactions. We extracted the odds ratios (OR) with their 95% confidence intervals (95%CI) of each clinical studiy reported in the included published meta-analyses.

Summary measures

We used odds ratio (OR) and its 95% confidence interval (95%CI) to estimate the association between the genotype and the risk of the drug induced adverse reaction.

Synthesis of result–data analysis

For each unique triplet association _{[genotype-drug-ADR]}, we gathered the ORs of the clinical studies from different published meta-analyses. We limited the compilation to comparisons using treated patients as control groups.

We used the funnel plot approach for assessing potential publication bias [22, 23]. We generated the funnel plots for each included associations, for estimating the prevalence of publication bias. Firstly, we conducted a visual analysis of the generated funnel plots [24]. Two researcher independently assessed if a publication bias was ‘likely’, ‘unlikely’, or ‘not determinable’ (SB, GG). Agreement was estimated using a Free-marginal kappa estimator [25]. A third researcher helped resolve disagreements, blinded to the previous diagnoses (JC). Secondly, asymmetry was tested using the Egger [26] and Begg’s methods [27] (function metabias, package {meta}) if at least five studies were available. P-value <0.05 were considered significant without adjustment for multiple testing. We calculated and reported the proportion of publication bias according to these three methods. Given the guidance of Cochrane handbook, the fail-safe number method was not used [22].

For exploring the potential impact of publication bias, we used the Trim and Fill [28] method to adjust the OR for potential publication bias [29]. We applied the Trim and Fill function (estimator L, fixed-effect model) if more than five estimates and their 95%CI were available for the same association _{[genotype-drug-ADR]}). Then, we compared the OR obtained without adjustment for publication bias (OR_NP) and the OR estimated with the Trim and Fill method (OR_TM). We also tested for an interaction between each pair of ORNP−OR_TM, using the ratio of the ORs (ROR) and its 95%CI. We conducted the analyses on R 3.3.1 [30] (package {meta}, version 4.9–4) [31].

Results

Study selection

From 295 references identified on PubMed, we included ten systematic reviews (see Fig 1). From them, we removed one systematic review [32] that contained estimates with infinite confidence intervals, limiting their use in our study. The nine usable systematic reviews included 33 published meta-analyses [15–18, 33–37]. Among them, 22 unique triplet associations _{[genotype-drug-ADR]} were available for analysis.

Study characteristics

The characteristics of included meta-analyses (published meta-analyses and each specific triplet association _{[genotype-drug-ADR]}) are detailed in the Table 1. In addition to the first author with the corresponding bibliographic reference of the published systematic review, a specific ‘identification number’ identified each published meta-analysis, a specific letter identified each specific triplet association _{[genotype-drug-ADR]}. Most of the included patients were Asian. Most of the gene variants were related to the HLA system. One published meta-analysis addressed the genetic polymorphisms of CYP2C9. Six antiseizure drugs were assessed (carbamazepine, lamotrigine, levetiracetam, phenobarbital, phenytoin, and valproate). The reported ADR were: “hypersensitivity”, hypersensitive syndrome (HSS), maculopapular exanthema (MPE), serious cutaneous reactions (SCRs), SJS, and TEN. All the selected meta-analyses included case-control study design. Used comparators were: treated but tolerant patients in 28 published meta-analyses, untreated patients in four published meta-analyses, and both in one published meta-analysis.

Table 1. Characteristics of included meta-analyses.

Author	IdP	Genotype	Antiseizure drug	ADR	Control	Ethnicity	Association
Chouchi [15]	1	HLA-B*15:02	Carbamazepine	SCRs	T	Mostly Asian	A
Chouchi [15]	2	HLA-B*15:02	Carbamazepine	SJS	T	Mostly Asian	B
Chouchi [15]	3	HLA-B*15:02	Carbamazepine	SJS, TEN	T	Mostly Asian	C
Grover [16]	10	HLA-B*15:02	Carbamazepine	SJS, TEN	T	Asian	C
Tangamornsuksan [34]	23	HLA-B*15:02	Carbamazepine	SJS, TEN	U&T	Asian	C
Yip [37]	31	HLA-B*15:02	Carbamazepine	SJS, TEN	T	Asian	C
Deng [33]	4	HLA-B*15:02	Lamotrigine	SJS, TEN	T	Asian	D
Grover [16]	17	HLA-B*15:02	Lamotrigine	SJS, TEN	T	Asian	D
Li [18]	22	HLA-B*15:02	Lamotrigine	SJS, TEN	T	Asian	D
Zeng et al [17]	33	HLA-B*15:02	Lamotrigine	SJS, TEN	T	Asian	D
Grover [16]	18	HLA-B*15:02	Valproate	SJS, TEN	T	Asian	E
Grover [16]	19	HLA-B*15:02	Phenobarbital	SJS, TEN	T	Asian	F
Grover [16]	20	HLA-B*15:02	Levetiracetam	SJS, TEN	T	Asian	G
Grover [16]	16	HLA-B*15:02	Phenytoin	SJS, TEN	T	Asian	H
Li [18]	21	HLA-B*15:02	Phenytoin	SJS, TEN	T	Asian	H
Deng [33]	5	HLA-B*15:02	Lamotrigine	MPE	T	Asian	I
Grover [16]	11	HLA-B*15:02	Carbamazepine	HSS, MPE	T	Asian	J
Deng [33]	6	HLA-B*24:02	Lamotrigine	SJS, TEN	T	Asian	K
Deng [33]	7	HLA-B*24:02	Lamotrigine	MPE	T	Asian	L
Deng [33]	8	HLA-B*33:03	Lamotrigine	MPE	T	Asian	M
Deng [33]	9	HLA-B*58:01	Lamotrigine	MPE	T	Asian	N
Wang [35]	28	HLA-B*58:01	Carbamazepine	SJS, TEN	T	Asian	O
Grover [16]	13	HLA-A*31:01	Carbamazepine	HSS, MPE	T	Asian, European and American	P
Grover [16]	12	HLA-A*31:01	Carbamazepine	SJS, TEN	T	Asian, European and American	Q
Yip [37]	32	HLA-A*31:01	Carbamazepine	Hypersensitivity	T	Asian and European	R
Wang [35]	24	HLA-B*40:01	Carbamazepine	SJS, TEN	T	Asian	S
Wang [35]	26	HLA-B*15:11	Carbamazepine	SJS, TEN	T	Asian	T
Wang [35]	27	HLA-B*46:01	Carbamazepine	SJS, TEN	T	Asian	U
Wu [36]	29	CYP2C9*3	Phenytoin	SJS, TEN	T	Asian	V
Grover [16]	14	HLA-B*15:02	Carbamazepine	SJS, TEN	U	Asian	^*
Grover [16]	15	HLA-B*15:02	Carbamazepine	HSS, MPE	U	Asian	^*
Wang [35]	25	HLA-B*15:11	Carbamazepine	SJS, TEN	U	Asian	^*
Wu [36]	30	CYP2C9*3	Phenytoin	SJS, TEN	U	Asian	^*

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On the right: author’s column of the nine included systematic review; identification number (IdP) of the 33 published meta-analysis. On the left: association’s column: the 22 unique associations of the same triplet _{[genotype -drug-ADR]}. Rows are ordered by alphabetical order of the association’s column. ADR: adverse drug reaction; HSS: hypersensitive syndrome; MPE: Maculopapular exanthema, SCRs: Serious Cutaneous Reactions, SJS: Stevens—Johnson syndrome, TEN: Toxic Epidermal Necrolysis. Control group: treated tolerant patients (T) and/or untreated patients (U).

*: the comparisons using untreated patients as control were not included in the association’s analysis

Prevalence of the publication bias

Twenty-two funnel plots of specific associations _{[genotype-drug-ADR]} were generated. The funnels plots are available in the S1 Fig in S1 File. The Table 2 summarises the estimation of the prevalence of publication bias.

Table 2. Prevalence of the publication bias.

Association	Genotype	Drug	ADR	Number of studies	Funnel plots analyses
					Visual inspection	Egger’s p value	Begg’s p value
A	HLA-B*15:02	Carbamazepine	SCRs	9	Likely	0.22	0.02*
B	HLA-B*15:02	Carbamazepine	SJS	4	ND	NA	NA
C	HLA-B*15:02	Carbamazepine	SJS, TEN	18	Likely	0.12	0.24
D	HLA-B*15:02	Lamotrigine	SJS, TEN	7	Likely	0.06	0.22
E	HLA-B*15:02	Valproate	SJS, TEN	1	ND	NA	NA
F	HLA-B*15:02	Phenobarbital	SJS, TEN	1	ND	NA	NA
G	HLA-B*15:02	Levetiracetam	SJS, TEN	1	ND	NA	NA
H	HLA-B*15:02	Phenytoin	SJS, TEN	5	Likely	0.03*	0.14
I	HLA-B*15:02	Lamotrigine	MPE	6	Unlikely	0.94	0.57
J	HLA-B*15:02	Carbamazepine	HSS, MPE	5	Likely	0.97	0.62
K	HLA-B*24:02	Lamotrigine	SJS, TEN	2	ND	NA	NA
L	HLA-B*24:02	Lamotrigine	MPE	2	ND	NA	NA
M	HLA-B*33:03	Lamotrigine	MPE	2	ND	NA	NA
N	HLA-B*58:01	Lamotrigine	MPE	3	ND	NA	NA
O	HLA-B*58:01	Carbamazepine	SJS, TEN	5	ND	NA	0.62
P	HLA-A*31:01	Carbamazepine	HSS, MPE	6	ND	0.35	0.57
Q	HLA-A*31:01	Carbamazepine	SJS, TEN	6	Unlikely	0.41	0.57
R	HLA-A*31:01	Carbamazepine	Hypersensitivity	4	ND	NA	NA
S	HLA-B*40:01	Carbamazepine	SJS, TEN	6	Unlikely	0.24	0.19
T	HLA-B*15:11	Carbamazepine	SJS, TEN	2	ND	NA	NA
U	HLA-B*46:01	Carbamazepine	SJS, TEN	3	ND	NA	NA
V	CYP2C9*3	Phenytoin	SJS, TEN	3	ND	NA	NA

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Association: identification letter of the association (_{[genotype-drug-ADR]}). ADR: adverse drug reaction (HSS: hypersensitive syndrome; MPE: Maculopapular exanthema, SCRs: Serious Cutaneous Reactions, SJS: Stevens—Johnson syndrome, TEN: Toxic Epidermal Necrolysis), NA: not available.

Funnel plots analyses: visual assessment—publication bias is ‘likely’, ‘not determinable’ (ND), “unlikely”; p-values of Egger’s and of Begg’s tests (* stands for p<0.05).

Visual diagnosis of funnel plots

For the visual analysis of the funnel plots, before reaching consensus with the third reviewer, the percentage of overall agreement between the two initial reviewers was 64%, Free-marginal kappa = 0.45 [95%CI: 0.15; 0.76]. Our visual analysis of generated funnel plot estimated that a publication bias was i) “likely” in five (23% [95%CI: 8; 45]) out of 22 exploitable funnel plots; ii) “unlikely” in 3 (14% [95%CI: 3; 35]) out of 22 exploitable funnel plots, and iii) “not determinable” in 14 (64% [95%CI: 41; 83]) out of 22 exploitable funnel plots.

Assymetry tests

The Egger’s test i) showed a significant (p<0.05) publication bias in one out of nine assessable funnel plots (11% [95%CI: 0; 48]). It was not able to conclude (p>0.05) in eight out of nine assessable funnel plots (89% [95%CI: 52; 100]). The funnel plots were not exploitable in 13 out of 22 cases (59% [95%CI: 36; 79]).

The Begg’s test showed a significant (p<0.05) publication bias in one out of 10 assessable funnel plots (10% [95%CI: 0; 45]). It was not able to conclude (p>0.05) in 9 out of 10 assessable funnel plots (90% [95%CI: 55; 100]). It was not assessable in 12 out of 22 exploitable funnel plots (55% [95%CI: 32; 76]).

The two significant publication bias identified by the asymmetry tests affected the associations of HLA-B*15:02 and carbamazepine induced SCRs (Begg’s test, p-value = 0.02, see Fig 2) and SJS and TEN related to phenytoin (Egger’s test, p = 0.03, see Fig 3).

Fig 2 — Each point is a clinical studies. The white, dark, and light grey zones stand for a p value of the odds ratio i) non-significant, ii) between 0.05 and 0.01, and iii) <0.01, respectively. The dashed triangle stands for the estimation of the meta-analysis of the association, without adjusting for a potential publication bias.

Fig 3 — Each point is a clinical studies. The white, dark, and light grey zones stand for a p value of the odds ratio i) non-significant, ii) between 0.05 and 0.01, and iii) <0.01, respectively. The dashed triangle stands for the estimation of the meta-analysis of the association, without adjusting for a potential publication bias.

Exploration of the impact of publication bias

The Trim and Fill estimates were calculable for 7 out the 22 funnel plots. Most of the associations suggested an increased risk of antiseizure drug’s AE with the pharmacogenetic biomarkers. The size of the associations were highly modified when taking into account a potential publication bias; the quantitative impact ranged from halving to doubling the estimation of the association. No association was qualitatively modified by taking into account the publication bias. The interaction tests between OR_NP and OR_TM were not significant.. The OR_NP, OR_TM, and ROR are detailed in the Table 3.

Table 3. Exploration of the impact of publication bias.

Association	Genotype	Drug	ADR	k	OR_NP	[95%CI]_NP	OR_TM	[95%CI]_TM	ROR	[95%CI]_Inter
A	HLA-B*15:02	Carbamazepine	SCRs	9	27.32	[9.93; 75.17]^*	14.66	[4.82; 44.61]^*	1.86	[0.41; 8.39]
C	HLA-B*15:02	Carbamazepine	SJS, TEN	18	39.77	[20.96; 75.48]^*	21.15	[10.49; 42.67]^*	1.88	[0.73; 4.86]
D	HLA-B*15:02	Lamotrigine	SJS, TEN	7	4.41	[1.82; 10.68]^*	3.99	[1.69; 9.44]^*	1.11	[0.32; 3.80]
I	HLA-B*15:02	Lamotrigine	MPE	6	1.19	[0.47; 3.00]	1.13	[0.47; 3.00]	1.05	[0.28; 3.91]
P	HLA-A*31:01	Carbamazepine	HSS, MPE	6	8.82	[5.74; 13.56]^*	8.82	[5.74; 13.56]^*	1.00	[0.54; 1.84]
Q	HLA-A*31:01	Carbamazepine	SJS, TEN	6	6.46	[2.01; 20.80]^*	12.22	[3.39; 44.05]^*	0.53	[0.09; 3.00]
S	HLA-B*40:01	Carbamazepine	SJS, TEN	6	0.28	[0.14; 0.54]^*	0.24	[0.11; 0.53]^*	1.17	[0.41; 3.29]

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Association: identification letter of each association _{[genotype-drug-ADR]}. ADR: adverse drug reaction (HSS: hypersensitive syndrome; MPE: Maculopapular exanthema, SCRs: serious cutaneous reactions, SJS: Stevens—Johnson syndrome, TEN: Toxic Epidermal Necrolysis), k: number of clinical studies involved. OR [95%CI]: odds ratio and its 95% confidence interval, Not adjusted for potential Publication bias (NP), estimated using the Trim and Fill method (TM) (when number of clinical studies >5), calculated for the interaction between not adjusted and adjusted ROR (Inter) (the ratio of ORs and its 95%CI).

* indicates 95%IC excluding the absence of association (i.e. the value 1.00)

Discussion

Summary of evidence

Most of the published meta-analyses on pharmacogenetic biomarkers of antiseizure drug’s AE reported cutaneous complications. We showed that a publication bias was not rare in the assessment of pharmacogenetic biomarkers of antiseizure drug’s AE. The visual analysis of the funnel plots showed that a publication bias might affect almost one quarter of those associations in this field. Using asymmetry tests, we showed that about 10% of those associations were subject to a significant publication bias. We showed that taking into account a potential publication bias might double or halve the estimation of the risk of antiseizure drug’s AE associated with those genetic biomarkers.

Our results suggested a significant publication bias for the HLA-B*15–2 and its association with the risk of carbamazepine-induced serious cutaneous reactions and of phenytoin-induced SJS or TEN. This may challenge the Clinical Pharmacogenetics Implementation Consortium guidelines for carbamazepine and phenytoin [10, 11] and should be discussed in the other European pharmacogenetic networks (such as the French Network of Pharmacogenetics—RNPGx—, the Dutch Pharmacogenetics Working Group—DPWG—). However, the risk of SJS, TEN, and MPE with carbamazepine remained highly increased by the presence of the HLA-B*15:02 genotype, even when taking into account the publication bias. The effect of these associations remains high (from ≈4 to ≈40), not negating their use in clinical practice. Unfortunately, the present study was limited by the lack of power related to the study number, notably for adjusting the estimates of the association between HLA-B*15:02 and phenytoin related ADR. Finally, adjusting for publication bias affected the estimation of the pharmacogenetic associations. Indeed, even for the association between the genetic variant HLA-B*15:02 and the risk of SCRs and SJS in people treated with carbamazepine, the increase of risk appears to be overestimated by two-folds. In contrast, the association between the genetic variant HLA-A*31:01 and the risk of SJS in people treated with carbamazepine seems to be underestimated by a factor two when taking into account a potential publication bias (OR_NP of 6 versus OR_TM of 12), surprisingly suggesting potential highly significant unpublished clinical studies.

Strengths of the study

To our knowledge, our study is the first meta-epidemiological assessment of the publication bias in the pharmacogenetics of antiseizure drug adverse reactions. We used a systematic umbrella review, allowing us to gather the information from overlapping published meta-analyses and to increase the sample size. We provided an estimation of the prevalence of the publication bias in this field, using several assessments of publication bias, including independent visual analyses of the funnel plots. We also explored the impact of publication bias on the size of the effect of such association, and finally discussed the potential consequences of those results on guidelines for clinical implementation of pharmacogenetics.

Limitations

However, our study presents several limits. First, we used the visual analysis of funnel plots, which is exposed to subjectivity. However, there is no consensual approach available for a publication bias assessment [22, 23]. This limitation illustrates the need for new tools for a publication bias assessment. Moreover, publication bias is not the only source of funnel plot asymmetry. Especially, a poor methodological quality of the small-included studies might led to the “small study effect”. A true heterogeneity also might contribute to the funnel asymmetry. However, in our study, some of the punctual estimates seem to line up with usual cut-off p values for significance (see notably Fig 2, along the 1% cut off); the use of contour-enhanced funnel plots allowed to highlight that the asymmetry seems to be associated with the significance of the included studies. Moreover, we believed that the heterogeneity is limited in our funnel plots, as we kept the most precise granularity, notably by not gathering different, despite close, adverse drug reactions, in line with the author’s definitions. Furthermore, tools for a publication bias assessment, as the Egger’s test for example, have been initially developed for a meta-analysis of randomized trials. However, most of the available pharmacogenetic studies here were non-randomized. If the same tools for publication bias assessment may be used in meta-analysis of such non-randomized studies remains unclear. Second, despite our umbrella review approach, the number of clinical trials available remained limited for several associations. It limited the assessment of publication bias, as illustrated by the significant number of “not determinable” assessment. We were not able to provide publication bias adjusted estimates of the association between HLA-B*15:02 and phenytoin related ADR, especially. We did not conduct additional searches of original clinical studies. Indeed, focusing on published systematic review allowed assessing the impact of the publication bias in the currently available meta-analyses, which are used for guideline elaboration. We also did not remove the associations with a low number of estimates, leading to report results of little utility. Indeed, we did not consider exclusion criteria based on the number of point estimates. Therefore, we reported all the associations. Such exclusion criteria would require an arbitrary cut-off, which might be questionable. Above all, such exclusion criteria might lead to overestimate the prevalence of publication bias in the field. It would have been possible to gather together some associations, some of whom displayed similar ADRs (as “SCRs”, “SJS”, “SJS, TEN” considered in different associations). Indeed, some logical grouping are probably legit and relevant. This would have increased the power of the analysis. For example, combining the association A, B and C ([HLA-B*15:02 –carbamazepine–SCR/SJS/SJS,TEN] allowed to reach a nominal p value <0.05 for both the tests of Egger (p = 0.0009) and of Begg (p = 0.02) (S2 Fig in S1 File). However, this would have probably lead to overestimate the prevalence of the publication bias in the field. Therefore, we preferred a more conservative approach by respecting the reported ADRs as defined by the authors of the clinical studies and of the published meta-analyses, despite the possible decrease of the power of our analysis. Third, the asymmetry of funnel is not entirely specific of the presence of a publication bias and can be related to heterogeneity in treatment effects. We also used a low number of studies as cut-off for using asymmetry tests. Fourth, we also used the Trim and Fill method, which requires some assumptions: it simulates potential missing studies as mirror images of observed studies [38]. Furthermore, most of the included clinical studies were at high risk of non-publication bias, including confusion and selection bias. Indeed, most of those genetic biomarker are in fact prognostic biomarkers of drug adverse reactions in a treated population. Moreover, we did not study the potential effect of subpopulations on those pharmacogenetics associations [12]. Furthermore, we used the genotype’s definition reported in the published systematic review, even when rs number were not available. Last but not least, a publication bias for other associations might exist and remains invisible if most of the corresponding studies are not published at all.

Implications of the study

Our study showed that,—as in clinical pharmacology [3] or in genetics [4]—, publication is selective in pharmacogenetics. We showed that this publication bias may affect the assessment of an association between genetic biomarker and ADR, even for consensual pharmacogenetic biomarkers of antiseizure drug’s AE as HLA-B*15:02. Our study showed the need for publishing or at least registering any pharmacogenetics study, even with inconclusive results, to fight the issue of publication bias and its harmful consequences. The increased access to genomic databases and to genomic data through next generation sequencing strengthens the importance to anticipate such issues.

Conclusion

Both the genomic area and the drug safety assessment are prone to a high risk of false positive results. Publication bias may contribute to the canonisation of such false positive associations. This may lead to not prescribing efficient drugs for false reasons, and to insufficient control of epilepsy. Moreover, false positive results dilute the true safety signal. Taking into account the publication bias is needed for correctly estimating the personalized benefit risk—balance of antiseizure drugs. Complementary to the recent “Strengthening the Reporting Of Pharmacogenetic Studies: Development of the STROPS guideline” [39], publication of negative pharmacogenetic studies is required.

Supporting information

S1 File

(DOCX)

Click here for additional data file.^{(2MB, docx)}

S1 Data

(7Z)

Click here for additional data file.^{(45.1KB, 7z)}

Acknowledgments

We acknowledge Berthe El-Aya for her assistance in editing the present article.

Abbreviations

ADRs: adverse drug reactions
AE: adverse effect
CI: Confidence Interval
HSS: hypersensitive syndrome
IdP: identification number
MPE: maculopapular exanthema
ND: not determinable
OR: OR obtained without adjustment for publication bias
ORs: odds ratio
OR_TM: OR estimated with the Trim and Fill method
PGx: pharmacogenetic biomarkers
pMA: published meta-analyses
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
ROR: ratio of the ORs
SCRs: serious cutaneous reactions
SJS: Stevens-Johnson syndrome
T: treated tolerant patients
TEN: toxic epidermal necrolysis
TM: Trim and Fill method
U: untreated patients

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0278839.r001

Decision Letter 0

Thomas Phillips

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5 Jul 2022

PONE-D-21-20495Publication bias in pharmacogenetics of adverse reaction to antiepileptic drugs: an umbrella review and a meta-epidemiological studyPLOS ONE

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Reviewer #1: In the manuscript "Publication bias in pharmacogenetics of adverse reaction to antiepileptic drugs: an umbrella review and a meta-epidemiological study", the authors describe an umbrella review of systematic reviews and meta-analyses of pharmacogenetic associations with adverse drug reactions to antiepileptic medications. The authors limited their review to previously published meta-analyses and systematic reviews to identify gene-drug-adverse reaction associations with multiple studies. The authors were able to identify 22 distinct associations which were analyzed for publication bias using funnel plots and tests of asymmetry. Overall, the authors make an attempt at addressing a potentially important problem when making clinical guidelines with regard to pharmacogenetic risks when taking antiepileptics. I agree with the final conclusion that studies of pharmacogenetic interactions should likely be registered even if negative in order to provide complete data. There are several concerns from this reviewer, however.

1. There are multiple grammar and word choice errors throughout the manuscript that are distracting at best but may actually affect the author's meaning or intent in certain instances at worst. A thorough, careful editing is necessary to make the manuscript more readable and ensure clear communication to the reader.

2. With regard to the author's methods, it is understandable that an umbrella review of meta-analyses and systematic reviews was utilized in order to exclude one-off pharmacogenetic interactions, but it is unclear why the authors did not conduct additional searches for specific interactions identified from the reviews in order to ensure that all publications were identified. Furthermore, if the intent was to exclude interactions with low numbers of publications, why were interactions with only one publication mentioned in a review included as identified interactions?

3. The authors state that 14 of 22 funnel plots were "not determinable" (line 231). Many of these "not determinable" funnel plots have low number of studies (table 2 and supplementary data). It was noted that all interactions with 1-4 studies were ND; 4 or less is studies is low enough to likely make funnel plot examination of little utility. It is unclear why these interactions were not simply outright excluded.

4. The reviewer is also unclear why interactions are studied so specifically. Given that there are cutaneous reactions of varying severity associated with overlapping genotypes and medications, would attempting a meta-analysis of all reactions for one variable (genotype, SJS) or combination of variable (e.g. SJS and HLA type) give better power to study publication bias in the field. On a related note, do the adverse drug reactions represent varying degrees of severity on a spectrum of cutaneous reactions or are the less severe reactions mediated by different mechanism and therefore have a specific dermatologic/histologic diagnosis? If they are all a similar mechanism, this would certainly allow for some logical grouping (e.g. all reactions in individuals with HLA-B*15:02 taking carbamazepine).

5. It is my understanding regarding the Egger's test that it is appropriate for randomized controlled trials but it is unclear if it is appropriate for studying pharmacogenetic interactions. The authors briefly mention in limitations that there are no established methods for studying publication bias in pharmacogenetics but this point is not specifically addressed.

Reviewer #2: This is very interesting paper addressing very important issue on publication bias in pharmacogenetic biomarkers (PGx) and antiepileptics adverse drug reactions (ADRs). This is very important topic and yet, have not received very much attention. The authors have confirmed that publication bias in the pharmacogenetic of antiepileptic ADRs is not uncommon and may affect the estimation of the effect of such biomarkers. I have a minor question that needs a clarification.

The results from this present study suggested that there is a significant publication bias for the HLA-B*15-2 and its association with the risk of carbamazepine-induced SCARs. Could you please clarify how to interpret this bias despite the fact that the use of HLA-B*15-02 screening has led to dramatically decrease in the incidence of SJS/TEN in Southeast Asia? How should we use this data in clinical practice?

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Dec 30;17(12):e0278839. doi: 10.1371/journal.pone.0278839.r002

Author response to Decision Letter 0

10 Oct 2022

[For clarity purpose, you'll find the respons in a specific .doc file, including special text formatting]

PLOS ONE Decision: Revision required [PONE-D-21-20495] - [EMID:04cdbb83f48c962a]

Response to Reviewers

Dear Editor, Dear Reviewers,

Thank you very much for your time and advices. Please find below our corrections following your comments.

Of note, we realized that the initial calculation of the ratio of ORs, the ROR, omitted to take into account the variance of the adjusted ORs (ORTM). We corrected the corresponding results section “Exploration of the impact of publication bias” by removing the sentence “The interaction test for the association of [HLA-B*15:02-carbamazepine-SJS/TEN] (identification letter #C in Table 3) almost reach significativity (ORNP=39.77 [20.96; 75.48]; ORTM =21.15 [10.49; 42.67]; interaction test: ROR=1.88 [0.99; 3.57]).” and the table 3. Fortunately, this does not change the results, as the confidence intervals of the ROR were already not significant, they just became larger. We sincerely apologize for this mistake.

Best regards,

The corresponding author on behalf of the authors

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

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ANSWER

My bad, SB has not been funded by a specific project grant, it was a reference to its legally required traineeship grant. We corrected “The author(s) received no financial support for the research, authorship, and/or publication of this article.”

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3. Thank you for stating the following financial disclosure: "Funding

SB was funded by the UMR 5558, Université Lyon 1, CNRS (https://www.univ-lyon1.fr/).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

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ANSWER

My bad, there was no specific funding. We corrected “The author(s) received no financial support for the research, authorship, and/or publication of this article.”

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b. Confirm in your cover letter that you agree with the following statement, and we will change the online submission form on your behalf:

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ANSWER

My bad, there was no specific funding. We corrected “The author(s) received no financial support for the research, authorship, and/or publication of this article.”

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4. Thank you for stating the following in the Competing Interests section: "Conflict of interest

SB, JC, MGC, GG declare that they have no competing interest.

JCL has received speaking fees and honoraria from Roche.

CV has received consulting fees from Genzyme, Novartis and speaker honoraria from Galapagos.

SR received speaker and/or consulting fees from UCB Pharma, EISAI, GW Pharma, Idorsia, LivaNova, and Arvelle Therapeutics.

GL has received speaker honoraria from GWpharma, Eisai and Biomarin.

MC has received consulting fees from Boehringer Ingelheim, SANOFI, AstraZeneca, EISAI and speaker honoraria from SANOFI."

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ANSWER

We added the PLOS ONE sentence “I have read the journal's policy and the authors of this manuscript have the following competing interests:”

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Please include your amended Competing Interests Statement within your cover letter. We will change the online submission form on your behalf.

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ANSWER

We added the sentence “This does not alter our adherence to PLOS ONE policies on sharing data and materials.”

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5. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found.

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ANSWER

The sentence “All relevant data are within the manuscript and its Supporting Information files.” Might be corrected as the following: “All relevant data are within the manuscript and its Supporting Information 4: Results of individual studies”

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ANSWER

Done

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Reviewers' comments:

5. Review Comments to the Author

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ANSWER

Thanks, we sent the paper to our university team for language editing.

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ANSWER

Thanks, we added:

“We did not conduct additional searches of original clinical studies. Indeed, focusing on published systematic review allow assessing the impact of the publication bias in the currently available meta-analyses, which are used for guidelines elaboration. ”

And please see answer to question 3

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ANSWER

Thanks, we added:

“We also did not remove the associations with a low number of estimates, leading to report results of little utility. Indeed, we did not consider exclusion criteria based on the number of point estimates. Therefore, we reported all the associations. Such exclusion criteria would require an arbitrary cut-off, which might be questionable. Above all, such exclusion criteria might lead to overestimate the prevalence of publication bias in the field. ”

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ANSWER

Thanks, we added:

“Indeed, some logical grouping are probably legit and relevant. This would have increase the power of the analysis. For example, combining the association A, B and C ([HLA-B*15:02 – carbamazepine – SCR/SJS/SJS,TEN] allowed to reach a nominal p value <0.05 for both the tests of Egger (p = 0.0009) and of Begg (p = 0.02) (supplementary figure S2). However, this would have probably lead to overestimate the prevalence of the publication bias in the field. Therefore,… ”

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ANSWER

Thanks, we added:

“Moreover, tools for publications bias assessment, as the Egger's test for example, have been initially developed for meta-analysis of randomized trials. However, most of the available pharmacogenetic studies here were non-randomized. If the same tools for publications bias assessment may be used in meta-analysis of such non-randomized studies remains unclear”

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ANSWER

Thanks, we added:

“The effect size of these associations remain high (from ≈4 to ≈40), not negating their use in clinical practice. ”

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Attachment

Submitted filename: PONE-D-21-20495 rev1_vf.docx

Click here for additional data file.^{(22.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0278839.r003

Decision Letter 1

Huijuan Cao

10 Nov 2022

PONE-D-21-20495R1Publication bias in pharmacogenetics of adverse reaction to antiseizure drugs: an umbrella review and a meta-epidemiological studyPLOS ONE

Dear Dr. Grenet,

Please submit your revised manuscript by Dec 25 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

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Huijuan Cao, Ph.D.

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #3: In the manuscript entitled "Publication bias in pharmacogenetics of adverse reaction to antiseizure drugs: an umbrella review and a meta-epidemiological study", the author analysed the error estimation of the correlation between pharmacogenetic biomarkers and antiseizure drug's adverse effects based on the discussion of publication bias. This is a very interesting article, and the research idea is very creative. After a revision of this manuscript, there is no obvious loophole in the article. Author's conclusions drawn seemed to be unbiased with rigorous methodology, therefore, I just give some minor comments on it.

1) The author only selected Medline database in the retrieval process, but did not seem to explain the specific reasons. I am afraid that some literature may be missing. Although the author uses other methods to supplement the search results except database retrieval, I believe that multiple relevant databases should be used in the retrieval process to avoid omission.

2) The Egger and Begg's methods are the most commonly used methods for funnel chart symmetry test, but publication bias is not the only source of asymmetry, such as methodological design and statistical analysis methods may affect it. Although the author mentioned this in the chapter of research limitations, I want to know the author's views on this issue, because in my opinion, this issue can reduce its impact on the results through some methods.

This question just makes me curious, because I have consulted some methodological articles, and the relevant items mentioned in them are not applicable to the author's research methods. I just made some guesses about whether subgroup analysis or meta regression can solve this problem.

**********

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Reviewer #1: No

Reviewer #3: Yes: Yi Yuan

**********

PLoS One. 2022 Dec 30;17(12):e0278839. doi: 10.1371/journal.pone.0278839.r004

Author response to Decision Letter 1

18 Nov 2022

PONE-D-21-20495R1_rev2

Dear Editor, Dear Reviewers,

Thank you very much for your time and advices. Please find below our corrections following your comments.

Best regards,

The corresponding author on behalf of the authors

6. Review Comments to the Author

Reviewer #1: (No Response)

-----

ANSWER: NA

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ANSWER: thank you very much!

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ANSWER: Indeed, we added the following in the methods section:

“We limited the information source to the Medline database. Indeed, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for HLA genotype and Use of Carbamazepine and Oxcarbazepine was restricted to the use of “the PubMed database”11. Using the same restriction allowed to estimate the impact of publication bias in the context of the current practice of guidelines elaboration in this area.”

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ANSWER: Indeed, we added the following in the discussion section:

“Moreover, publication bias is not the only source of funnel plot asymmetry. Especially, a poor methodological quality of the small-included studies might led to the “small study effect”. A true heterogeneity also might contribute to the funnel asymmetry. However, in our study, some of the punctual estimates seem to line up with usual cut-off p values for significance (see notably figure 2, along the 1% cut off); the use of contour-enhanced funnel plots allowed to highlight that the asymmetry seems to be associated with the significance of the included studies. Moreover, we believed that the heterogeneity is limited in our funnel plots, as we kept the most precise granularity, notably by not gathering different, despite close, adverse drug reactions, in line with the author’s definitions.”

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Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(14.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0278839.r005

Decision Letter 2

Huijuan Cao

24 Nov 2022

Publication bias in pharmacogenetics of adverse reaction to antiseizure drugs: an umbrella review and a meta-epidemiological study

PONE-D-21-20495R2

Dear Dr. Grenet,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Huijuan Cao, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

**********

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

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #3: Yes: Yi Yuan

**********

PLoS One. doi: 10.1371/journal.pone.0278839.r006

Acceptance letter

Huijuan Cao

20 Dec 2022

PONE-D-21-20495R2

Publication bias in pharmacogenetics of adverse reaction to antiseizure drugs: an umbrella review and a meta-epidemiological study

Dear Dr. Grenet:

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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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Huijuan Cao

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PLOS ONE

Associated Data

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

Supplementary Materials

S1 File

(DOCX)

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S1 Data

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Data Availability Statement

All relevant data are within the paper and its Supporting information files.

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PERMALINK

Publication bias in pharmacogenetics of adverse reaction to antiseizure drugs: An umbrella review and a meta-epidemiological study

S Bally

J Cottin

M C Gagnieu

J C Lega

C Verstuyft

S Rheims

G Lesca

M Cucherat

Guillaume Grenet

Roles

Abstract

Introduction

Rationale

Objectives

Methods

Protocol

Eligibility criteria

Information sources

Search strategy

Study selection and data extraction

Summary measures

Synthesis of result–data analysis

Results

Study selection

Fig 1. Flow diagram of the bibliographic search.

Study characteristics

Table 1. Characteristics of included meta-analyses.

Prevalence of the publication bias

Table 2. Prevalence of the publication bias.

Visual diagnosis of funnel plots

Assymetry tests

Fig 2. Funnel plot of the association between [HLA-B*15:02- Carbamazepine- Serious Cutaneous Reactions (SCRs)].

Fig 3. Funnel plot of the association between [HLA-B*15:02- Phenytoin- SJS/TEN (SJS: Stevens—Johnson syndrome, TEN: Toxic Epidermal Necrolysis)].

Exploration of the impact of publication bias

Table 3. Exploration of the impact of publication bias.

Discussion

Summary of evidence

Strengths of the study

Limitations

Implications of the study

Conclusion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Thomas Phillips

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Huijuan Cao

Roles

Author response to Decision Letter 1

Decision Letter 2

Huijuan Cao

Roles

Acceptance letter

Huijuan Cao

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Cited by other articles

Links to NCBI Databases

Fig 2. Funnel plot of the association between _{[HLA-B*15:02- Carbamazepine- Serious Cutaneous Reactions (SCRs)]}.

Fig 3. Funnel plot of the association between _{[HLA-B*15:02- Phenytoin- SJS/TEN (SJS: Stevens—Johnson syndrome, TEN: Toxic Epidermal Necrolysis)]}.