Employing a latent variable framework to improve efficiency in composite endpoint analysis. (March 2021)

• Record Type: Journal Article
• Authors: McMenamin, Martina; Barrett, Jessica K; Berglind, Anna; Wason, James MS
• Is Part Of: Statistical methods in medical research. Volume 30:Number 3(2021)
• Page Start: 702
• Page End: 716
• Publication Date: 2021-03
• Subjects: Composite endpoint -- latent variable model -- responder analysis -- systemic lupus erythematosus; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280220970986
• Languages: English
• ISSNs: 0962-2802

Abstract

Composite endpoints that combine multiple outcomes on different scales are common in clinical trials, particularly in chronic conditions. In many of these cases, patients will have to cross a predefined responder threshold in each of the outcomes to be classed as a responder overall. One instance of this occurs in systemic lupus erythematosus, where the responder endpoint combines two continuous, one ordinal and one binary measure. The overall binary responder endpoint is typically analysed using logistic regression, resulting in a substantial loss of information. We propose a latent variable model for the systemic lupus erythematosus endpoint, which assumes that the discrete outcomes are manifestations of latent continuous measures and can proceed to jointly model the components of the composite. We perform a simulation study and find that the method offers large efficiency gains over the standard analysis, the magnitude of which is highly dependent on the components driving response. Bias is introduced when joint normality assumptions are not satisfied, which we correct for using a bootstrap procedure. The method is applied to the Phase IIb MUSE trial in patients with moderate to severe systemic lupus erythematosus. We show that it estimates the treatment effect 2.5 times more precisely, offering a 60% reduction in required sample size.