Subgroup identification in individual participant data meta-analysis using model-based recursive partitioning
نویسندگان
چکیده
Model-based recursive partitioning (MOB) can be used to identify subgroups with differing treatment effects. The detection rate of treatment-by-covariate interactions and the accuracy identified using MOB depend strongly on sample size. Using data from multiple randomized controlled clinical trials overcome problem too small samples. However, naively pooling may result in identification spurious as differences study design, subject selection other sources between-trial heterogeneity are ignored. In order account for individual participant (IPD) meta-analysis random-effect models frequently used. Commonly, effect is modelled random effects whereas baseline risks by either fixed or this article, we propose metaMOB, a procedure generalized mixed-effects model tree (GLMM tree) algorithm subgroup IPD meta-analysis. Although application metaMOB potentially wider, e.g. experiments participants social sciences preclinical life sciences, focus trials. simulation study, outperformed GLMM trees assuming intercept only model-based (MOB), whose basis trees, respect false discovery rates, estimated effect. most robust therefore promising method modelling risks.
منابع مشابه
A recursive partitioning approach for subgroup identification in individual patient data meta-analysis.
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ژورنال
عنوان ژورنال: Advances in data analysis and classification
سال: 2021
ISSN: ['1862-5355', '1862-5347']
DOI: https://doi.org/10.1007/s11634-021-00458-3