Evaluation of biomarkers for treatment selection using individual participant data from multiple clinical trials.
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ABSTRACT:
SUBMITTER: Kang C
PROVIDER: S-EPMC5889758 | biostudies-literature | 2018 Apr
REPOSITORIES: biostudies-literature
Publications
Evaluation of biomarkers for treatment selection using individual participant data from multiple clinical trials.
Kang Chaeryon C Janes Holly H Tajik Parvin P Groen Henk H Mol Ben B Koopmans Corine C Broekhuijsen Kim K Zwertbroek Eva E van Pampus Maria M Franssen Maureen M
Statistics in medicine 20180214 9
Biomarkers that predict treatment effects may be used to guide treatment decisions, thus improving patient outcomes. A meta-analysis of individual participant data (IPD) is potentially more powerful than a single-study data analysis in evaluating markers for treatment selection. Our study was motivated by the IPD that were collected from 2 randomized controlled trials of hypertension and preeclampsia among pregnant women to evaluate the effect of labor induction over expectant management of the ...[more]
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Project description:Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and inverse-variance weighted meta-analysis methods. This is especially important as often different studies measure data on different variables, meaning that we may need to impute data on a variable which is systematically missing in a particular study. In this paper, we consider a simulation analysis of sporadically missing data in a single covariate with a linear analysis model and discuss how the results would be applicable to the case of systematically missing data. We find in this context that ensuring the congeniality of the imputation and analysis models is important to give correct standard errors and confidence intervals. For example, if the analysis model allows between-study heterogeneity of a parameter, then we should incorporate this heterogeneity into the imputation model to maintain the congeniality of the two models. In an inverse-variance weighted meta-analysis, we should impute missing data and apply Rubin's rules at the study level prior to meta-analysis, rather than meta-analyzing each of the multiple imputations and then combining the meta-analysis estimates using Rubin's rules. We illustrate the results using data from the Emerging Risk Factors Collaboration.
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