Characterizing Highly Benefited Patients in Randomized Clinical Trials

Vivek Charu, Paul B. Rosenberg, Lon S. Schneider, Lea T. Drye, Lisa Rein, David Shade, Constantine G. Lyketsos, Constantine E. Frangakis

Research output: Contribution to journalArticle

Abstract

Physicians and patients may choose a certain treatment only if it is predicted to have a large effect for the profile of that patient. We consider randomized controlled trials in which the clinical goal is to identify as many patients as possible that can highly benefit from the treatment. This is challenging with large numbers of covariate profiles, first, because the theoretical, exact method is not feasible, and, second, because usual model-based methods typically give incorrect results. Better, more recent methods use a two-stage approach, where a first stage estimates a working model to produce a scalar predictor of the treatment effect for each covariate profile; and a second stage estimates empirically a high-benefit group based on the first-stage predictor. The problem with these methods is that each of the two stages is usually agnostic about the role of the other one in addressing the clinical goal. We propose a method that characterizes highly benefited patients by linking model estimation directly to the particular clinical goal. It is shown that the new method has the following two key properties in comparison with existing approaches: first, the meaning of the solution with regard to the clinical goal is the same, and second, the value of the solution is the best that can be achieved when using the working model as a predictor, even if that model is incorrect. In the Citalopram for Agitation in Alzheimer's Disease (CitAD) randomized controlled trial, the new method identifies substantially larger groups of highly benefited patients, many of whom are missed by the standard method.

Original languageEnglish (US)
Article number20160045
JournalInternational Journal of Biostatistics
Volume13
Issue number1
DOIs
StatePublished - Jan 1 2017

Keywords

  • Alzheimer's disease
  • RCT
  • heterogeneity in treatment effects
  • high benefit

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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