title: Estimation of treatment effect in a subpopulation: An empirical Bayes approach.
abstract: It is well recognized that the benefit of a medical intervention may not be distributed evenly in the target population due to patient heterogeneity, and conclusions based on conventional randomized clinical trials may not apply to every person. Given the increasing cost of randomized trials and difficulties in recruiting patients, there is a strong need to develop analytical approaches to estimate treatment effect in subpopulations. In particular, due to limited sample size for subpopulations and the need for multiple comparisons, standard analysis tends to yield wide confidence intervals of the treatment effect that are often noninformative. We propose an empirical Bayes approach to combine both information embedded in a target subpopulation and information from other subjects to construct confidence intervals of the treatment effect. The method is appealing in its simplicity and tangibility in characterizing the uncertainty about the true treatment effect. Simulation studies and a real data analysis are presented.
authors.affiliation: a Department of Biostatistics , School of Medicine, Fairbanks School of Public Health, Indiana University , Indianapolis , Indiana , USA.
authors.forename: Changyu
authors.initials: C
authors.lastname: Shen
authors.affiliation: a Department of Biostatistics , School of Medicine, Fairbanks School of Public Health, Indiana University , Indianapolis , Indiana , USA.
authors.forename: Xiaochun
authors.initials: X
authors.lastname: Li
authors.affiliation: b Department of Statistics , Chonnam National University , Gwangju , Korea.
authors.forename: Jaesik
authors.initials: J
authors.lastname: Jeong
mesh_terms: D001499:Bayes Theorem; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic; D018401:Sample Size; D035501:Uncertainty
keywords: Causal inference; empirical bayes; heterogeneity in treatment effect; subgroup analysis