Cited by 113Meta-analysis and systematic reviewsStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

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: One-Stage IPD Meta-Analysis Models Must Avoid Ecological Bias

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Hua, Hairui, Burke, Danielle L., Crowther, Michael J., Ensor, Joie, Tudur Smith, Catrin, Riley, Richard D.·2017·Statistics in Medicine

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.

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Authors
Hua, H., Burke, D. L., Crowther, M. J., Ensor, J., Tudur Smith, C., & Riley, R. D.
Affiliations
  • Boehringer Ingelheim (China) (CN)
  • Keele University (GB)
  • University of Leicester (GB)
  • MRC North West Hub for Trials Methodology Research, Department of Biostatistics; University of Liverpool; Liverpool L69 3GL U.K.
Corresponding author
Riley, Richard D.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2016-12-01
Volume
36
Issue
5
Pages
772-789
ISSN
0277-6715 (ISSN-L)
OpenAlex
W2560126104
Cited by
113
References
3
Citations by year
2026: 6 · 2025: 11 · 2024: 5 · 2023: 14 · 2022: 10 · 2021: 35 · 2020: 8 · 2019: 11 · 2018: 8 · 2017: 4 · 2016: 1
FWCI
2.75
Type
journal article
Language
en
Open access
Locations
  • Statistics in Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Keele Research Repository (Keele University) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • Leicester Research Archive (University of Leicester) (submittedVersion, cc-by)
Funders
MRC Network of Hubs for Trials Methodology Research (MR/K025635/1)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
individual patient data meta-analysis, treatment-covariate interaction, ecological bias, stratified medicine, time-to-event data
MeSH terms
Age Factors, Anticonvulsants, Epilepsy, Female, Humans, Male, Sex Factors, Meta-Analysis as Topic, Models, Statistical, Bias, Confounding Factors, Epidemiologic, Randomized Controlled Trials as Topic, Treatment Outcome

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