Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed-effects models.
We propose statistical definitions of the individual benefit of a medical or behavioral treatment and of the severity of a chronic illness. These definitions are used to develop a graphical method that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine. The method focuses on assessing and comparing individual effects of treatments rather than average effects and can be used with continuous and discrete responses, including dichotomous and count responses. The method is based on new developments in generalized linear mixed-effects models, which are introduced in this article. To illustrate, analyses of data from the Sequenced Treatment Alternatives to Relieve Depression clinical trial of sequences of treatments for depression and data from a clinical trial of respiratory treatments are presented. The estimation of individual benefits is also explained. Copyright © 2016 John Wiley & Sons, Ltd.
Department of Biostatistics, The University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., Kansas City, 66160, KS, U.S.A.
Francisco J
FJ
Diaz
D001699:Biometry; D002908:Chronic Disease; D006801:Humans; D016014:Linear Models
chronic diseases; clinical trials; disease severity; empirical Bayesian prediction; generalized linear mixed-effects models; random effects linear models