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Model averaging for treatment effect estimation in subgroups
Author(s) -
Bornkamp Björn,
Ohlssen David,
Magnusson Baldur P.,
Schmidli Heinz
Publication year - 2016
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1796
Subject(s) - selection (genetic algorithm) , bayesian probability , model selection , clinical trial , selection bias , treatment effect , bayesian inference , econometrics , random effects model , computer science , statistics , mathematics , machine learning , medicine , artificial intelligence , meta analysis , traditional medicine
In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting “random high” / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem. The general motivation for the use of model averaging is to realize that subgroup selection can be viewed as model selection, so that methods to deal with model selection uncertainty, such as model averaging, can be used also in this setting. Simulations are used to evaluate the performance of the proposed approach. We illustrate it on an example early‐phase clinical trial.