Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints.
Pubmed ID: 25122189
Pubmed Central ID: PMC4263228
Journal: Biostatistics (Oxford, England)
Publication Date: 01/01/2015
Affiliation: Department of Biostatistics, Harvard University, Boston, MA 02115, USA.
MeSH Terms: Humans, Randomized Controlled Trials as Topic, Data Interpretation, Statistical, Risk Assessment, Adrenergic beta-Antagonists, Heart Failure, Precision Medicine, Outcome Assessment, Health Care
Grants: R01 AI024643, R01 AI052817, R01 GM079330, R01 HL089778, RC4 CA155940, U01 AI068616, U54 LM008748, UM1 AI068634, UM1 AI068636, UM1 AI068616
Authors: Tian L, Wei LJ, Claggett B, Castagno D
Cite As: Claggett B, Tian L, Castagno D, Wei LJ. Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints. Biostatistics 2015 Jan;16(1):60-72. Epub 2014 Aug 12.
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.