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.

Hairui Hua,Danielle L Burke,Richard D Riley,Catrin Tudur Smith,Michael J Crowther,Joie Ensor

doi:10.1002/sim.7171

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.

Highlights

There is an increasing interest in personalized or stratified medicine, where the aim is to tailor treatments to individuals or to groups of similar individuals based on their particular characteristics [1]
Fisher et al provide an excellent overview of methods for estimating interactions in meta-analysis [12], with illustration including survival examples; our work extends this through the detailed simulation study across a wide range of scenarios, with a novel example in epilepsy
Β^T performs best when there is no trial-level confounding, its performance deteriorates considerably when trial-level confounding exists as its estimate and coverage are severely affected by ecological bias, which may produce misleading conclusions

Summary

Introduction

There is an increasing interest in personalized or stratified medicine, where the aim is to tailor treatments to individuals or to groups of similar individuals based on their particular characteristics [1]. A Biostatistics & Data Sciences Asia, Boehringer Ingelheim, Shanghai, 200040, China bResearch Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire, ST5 5BG, U.K. c Department of Health Sciences, University of Leicester, Leicester LE1 7RH, U.K. dDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, S-171 77 Stockholm, Sweden e MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Liverpool L69.

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