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Statistics in Medicine
Research Article

Identification of subgroups with differential treatment effects for longitudinal and multiresponse variables

Wei‐Yin Loh

Corresponding Author

E-mail address: loh@stat.wisc.edu

Department of Statistics, University of Wisconsin, Madison, 53706 WI, U.S.A.

Correspondence to: Wei‐Yin Loh, Department of Statistics, University of Wisconsin, Madison, WI 53706, U.S.A.

E‐mail: loh@stat.wisc.edu

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Haoda Fu

Eli Lilly Company, Indianapolis, 46285 IN, U.S.A.

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Michael Man

Eli Lilly Company, Indianapolis, 46285 IN, U.S.A.

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Victoria Champion

School of Nursing, Indiana University, Indianapolis, 46202 IN, U.S.A.

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Menggang Yu

Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, 53706 WI, U.S.A.

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First published: 27 June 2016
https://doi.org/10.1002/sim.7020
Citations: 11
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Abstract

We describe and evaluate a regression tree algorithm for finding subgroups with differential treatments effects in randomized trials with multivariate outcomes. The data may contain missing values in the outcomes and covariates, and the treatment variable is not limited to two levels. Simulation results show that the regression tree models have unbiased variable selection and the estimates of subgroup treatment effects are approximately unbiased. A bootstrap calibration technique is proposed for constructing confidence intervals for the treatment effects. The method is illustrated with data from a longitudinal study comparing two diabetes drugs and a mammography screening trial comparing two treatments and a control. Copyright © 2016 John Wiley & Sons, Ltd.

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