title: One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information.
abstract: Stratified medicine utilizes individual-level covariates that are associated with a differential treatment effect, also known as treatment-covariate interactions. When multiple trials are available, meta-analysis is used to help detect true treatment-covariate interactions by combining their data. Meta-regression of trial-level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta-analyses are preferable to examine interactions utilizing individual-level information. However, one-stage IPD models are often wrongly specified, such that interactions are based on amalgamating within- and across-trial information. We compare, through simulations and an applied example, fixed-effect and random-effects models for a one-stage IPD meta-analysis of time-to-event data where the goal is to estimate a treatment-covariate interaction. We show that it is crucial to centre patient-level covariates by their mean value in each trial, in order to separate out within-trial and across-trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta-analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is -0.011 (95% CI: -0.019 to -0.003; p = 0.004), and thus highly significant, when amalgamating within-trial and across-trial information. However, when separating within-trial from across-trial information, the interaction is -0.007 (95% CI: -0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta-analysts should only use within-trial information to examine individual predictors of treatment effect and that one-stage IPD models should separate within-trial from across-trial information to avoid ecological bias. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
authors.affiliation: Biostatistics & Data Sciences Asia, Boehringer Ingelheim, Shanghai, 200040, China.
authors.forename: Hairui
authors.initials: H
authors.lastname: Hua
authors.affiliation: Research Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire, ST5 5BG, U.K.
authors.forename: Danielle L
authors.identifier: 0000-0003-2803-1151
authors.initials: DL
authors.lastname: Burke
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, U.K.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, S-171 77, Stockholm, Sweden.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Crowther
authors.affiliation: Research Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire, ST5 5BG, U.K.
authors.forename: Joie
authors.initials: J
authors.lastname: Ensor
authors.affiliation: MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, U.K.
authors.forename: Catrin
authors.initials: C
authors.lastname: Tudur Smith
authors.affiliation: Research Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire, ST5 5BG, U.K.
authors.forename: Richard D
authors.initials: RD
authors.lastname: Riley
mesh_terms: D000367:Age Factors; D000927:Anticonvulsants / Q000627:therapeutic use; D015982:Bias; D015986:Confounding Factors, Epidemiologic; D004827:Epilepsy / Q000188:drug therapy; D005260:Female; D006801:Humans; D008297:Male; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D012737:Sex Factors; D016896:Treatment Outcome
chemical_list: D000927:Anticonvulsants
keywords: ecological bias; effect modifier; meta-analysis; stratified/precision medicine; treatment-covariate interaction