title: Opportunities and challenges of combined effect measures based on prioritized outcomes.
abstract: Many authors have proposed different approaches to combine multiple endpoints in a univariate outcome measure in the literature. In case of binary or time-to-event variables, composite endpoints, which combine several event types within a single event or time-to-first-event analysis are often used to assess the overall treatment effect. A main drawback of this approach is that the interpretation of the composite effect can be difficult as a negative effect in one component can be masked by a positive effect in another. Recently, some authors proposed more general approaches based on a priority ranking of outcomes, which moreover allow to combine outcome variables of different scale levels. These new combined effect measures assign a higher impact to more important endpoints, which is meant to simplify the interpretation of results. Whereas statistical tests and models for binary and time-to-event variables are well understood, the latter methods have not been investigated in detail so far. In this paper, we will investigate the statistical properties of prioritized combined outcome measures. We will perform a systematical comparison to standard composite measures, such as the all-cause hazard ratio in case of time-to-event variables or the absolute rate difference in case of binary variables, to derive recommendations for different clinical trial scenarios. We will discuss extensions and modifications of the new effect measures, which simplify the clinical interpretation. Moreover, we propose a new method on how to combine the classical composite approach with a priority ranking of outcomes using a multiple testing strategy based on the closed test procedure.
authors.affiliation: Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 305, 69120, Heidelberg, Germany.
authors.forename: Geraldine
authors.initials: G
authors.lastname: Rauch
authors.forename: Antje
authors.initials: A
authors.lastname: Jahn-Eimermacher
authors.forename: Werner
authors.initials: W
authors.lastname: Brannath
authors.forename: Meinhard
authors.initials: M
authors.lastname: Kieser
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016016:Proportional Hazards Models; D016896:Treatment Outcome
keywords: composite endpoints; multiple testing; prioritized outcomes; time-to-event data; weighting procedure