title: Model averaging for treatment effect estimation in subgroups.
abstract: 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.
authors.affiliation: Novartis Pharma AG, Basel, CH-4002, Switzerland.
authors.forename: Björn
authors.initials: B
authors.lastname: Bornkamp
authors.affiliation: Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, 07936-1080, USA.
authors.forename: David
authors.initials: D
authors.lastname: Ohlssen
authors.affiliation: Novartis Pharma AG, Basel, CH-4002, Switzerland.
authors.forename: Baldur P
authors.initials: BP
authors.lastname: Magnusson
authors.affiliation: Novartis Pharma AG, Basel, CH-4002, Switzerland.
authors.forename: Heinz
authors.initials: H
authors.lastname: Schmidli
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D015983:Selection Bias; D035501:Uncertainty
keywords: Bayesian inference; exploratory study; proof of concept trial; shrinkage; subgroup analysis