title: Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints.
abstract: In a typical randomized clinical study to compare a new treatment with a control, oftentimes each study subject may experience any of several distinct outcomes during the study period, which collectively define the "risk-benefit" profile. To assess the effect of treatment, it is desirable to utilize the entirety of such outcome information. The times to these events, however, may not be observed completely due to, for example, competing risks or administrative censoring. The standard analyses based on the time to the first event, or individual component analyses with respect to each event time, are not ideal. In this paper, we classify each patient's risk-benefit profile, by considering all event times during follow-up, into several clinically meaningful ordinal categories. We first show how to make inferences for the treatment difference in a two-sample setting where categorical data are incomplete due to censoring. We then present a systematic procedure to identify patients who would benefit from a specific treatment using baseline covariate information. To obtain a valid and efficient system for personalized medicine, we utilize a cross-validation method for model building and evaluation and then make inferences using the final selected prediction procedure with an independent data set. The proposal is illustrated with the data from a clinical trial to evaluate a beta-blocker for treating chronic heart failure patients.
authors.affiliation: Division of Cardiovascular Medicine, Harvard Medical School, Boston, MA 02115, USA bclaggett@partners.org.
authors.forename: Brian
authors.initials: B
authors.lastname: Claggett
authors.affiliation: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.
authors.forename: Lu
authors.initials: L
authors.lastname: Tian
authors.affiliation: Division of Cardiology, Department of Medical Sciences, University of Turin, Turin 10124, Italy.
authors.forename: Davide
authors.initials: D
authors.lastname: Castagno
authors.affiliation: Department of Biostatistics, Harvard University, Boston, MA 02115, USA.
authors.forename: Lee-Jen
authors.initials: LJ
authors.lastname: Wei
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000627:therapeutic use; D003627:Data Interpretation, Statistical; D006333:Heart Failure / Q000188:drug therapy; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D057285:Precision Medicine / Q000379:methods*; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D018570:Risk Assessment
chemical_list: D000319:Adrenergic beta-Antagonists
keywords: Ordinal regression model; Personalized medicine; Subgroup analysis; Survival analysis