Journal
STATISTICS IN MEDICINE
Volume 36, Issue 5, Pages 772-789Publisher
WILEY
DOI: 10.1002/sim.7171
Keywords
ecological bias; effect modifier; meta-analysis; stratified/precision medicine; treatment-covariate interaction
Categories
Funding
- NIHR School for Primary Care Research Post-Doctoral Fellowship
- MRC Network of Hubs for Trials Methodology Research [MR/K025635/1]
- MRC [MR/K025635/1] Funding Source: UKRI
- National Institute for Health Research [SPCR-101, DRF-2012-05-409] Funding Source: researchfish
- National Institutes of Health Research (NIHR) [DRF-2012-05-409] Funding Source: National Institutes of Health Research (NIHR)
Ask authors/readers for more resources
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. (C) 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available