Sage Journals: Discover world-class research

Abstract

In clinical trials, it is often of interest to know whether treatment works differently for some groups than others, known as heterogeneity of treatment effect. Such subgroup analysis is complicated to conduct because trials are typically not powered to find subgroups. Furthermore, it is difficult to identify characteristics of patients pertaining to such subgroups. In this article, we propose a semiparametric mixture model to identify subgroups with time-to-event outcomes. Specifically, we assume a proportional hazards model with subgroup-specific piecewise constant baseline hazards, where the subgroup-specific treatment effect is assumed to be the same within each subgroup. The probability of belonging to a certain subgroup is a function of patient prognostic factors. Adopting a Bayesian approach, classification uncertainty is taken into account. We demonstrate the utility of our approach via simulation and an application to data from a real clinical trial in HIV research.

Keywords

Bayesian inference mixture models subgroup analysis survival analysis heterogenous treatment effect

Get full access to this article

View all access options for this article.

References

Imai

Ratkovic

. Estimating treatment effect heterogeneity in randomized program evaluation. Ann Appl Stat 2013; 7: 443–470.

Athey

Imbens

. Recursive partitioning for heterogeneous causal effects. Proc Natl Acad Sci U.S.A 2016; 113: 7353–7360.

Hill

. Bayesian nonparametric modeling for causal inference. J Comput Graph Stat 2011; 20: 217–240.

Hahn

Murray

Carvalho

. Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion). Bayesian Analysis 2020; 15: 965–1056.

Foster

Taylor

Ruberg

. Subgroup identification from randomized clinical trial data. Stat Med 2011; 30: 2867–2880.

Lyu

Kim

Suk

. Estimating heterogeneous treatment effects within latent class multilevel models: a Bayesian approach. J Educ Behav Stat 2023; 48: 3–36.

Zigler

Watts

Yeh

et al. Model feedback in Bayesian propensity score estimation. Biometrics 2013; 69: 263–273.

Altstein

Elashoff

. A method to estimate treatment efficacy among latent subgroups of a randomized clinical trial. Stat Med 2011; 30: 709–717.

Chu

Yuan

. BLAST: Bayesian latent subgroup design for basket trials accounting for patient heterogeneity. J R Stat Soc Ser C: Appl Stat 2018; 67: 723–740.

10.

Dunson

Pillai

Park

. Bayesian density regression. J R Stat Soc Ser B: Stat Methodol 2007; 69: 163–183.

11.

Barcella

De Iorio

Baio

. A comparative review of variable selection techniques for covariate dependent dirichlet process mixture models. Canadian J Stat 2017; 45: 254–273.

12.

Cox

. Partial likelihood. Biometrika 1975; 62: 269–276.

13.

Kass

Raftery

. Bayes factors. J Am Stat Assoc 1995; 90: 773–795.

14.

Berkhof

Van Mechelen

Gelman

. A Bayesian approach to the selection and testing of mixture models. Stat Sin 2003; 0: 423–442.

15.

Yao

Vehtari

Simpson

, et al. Using stacking to average Bayesian predictive distributions (with discussion). Bayesian Analy 2018; 13: 917–1007.

16.

Vehtari

Gelman

Gabry

. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput 2017; 27: 1413–1432.

17.

Vehtari

Gabry

Magnusson

, et al. LOO: efficient leave-one-out cross-validation and WAIC for Bayesian models. 2023. https://mc-stan.org/loo/.

18.

Carpenter

Gelman

Hoffman

, et al. Stan: a probabilistic programming language. J Stat Softw 2017; 76: 1–32.

19.

Hammer

Katzenstein

Hughes

, et al. A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. New England J Med 1996; 335: 1081–1090.

20.

Zhang

Zeng

. Variable selection for optimal treatment decision. Stat Methods Med Res 2013; 22: 493–504.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.10 MB

A Bayesian approach towards the identification of latent subgroups

Abstract

Keywords

Get full access to this article

References

Supplementary Material