Abstract
In practice, when both survival and quantitative outcomes are of interest, we encounter outcomes of mixed type: a censored outcome and a quantitative outcome. Joint modeling of the survival and quantitative outcomes rather than analyzing the outcomes separately has become a method of choice for analyzing mixed outcome data because of improved efficiency. However, the joint modeling provides two separate indexes for measuring the covariate (e.g., treatment) effect, making its interpretation difficult when the covariate inconsistently affects the quantitative and survival outcomes. By assigning a single rank to each outcome to represent the disease severity, this paper provides a unitary effect summary of the covariates on mixed outcome data while accounting for censoring. The method is applied to an analysis of the AIDS Clinical Trials Group protocol 175 (ACTG 175) data.
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