On the targets of inference with multivariate failure time data

Ross L. Prentice ()
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Ross L. Prentice: Fred Hutchinson Cancer Center

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 4, No 2, 546-559

Abstract: Abstract There are several different topics that can be addressed with multivariate failure time regression data. Data analysis methods are needed that are suited to each such topic. Specifically, marginal hazard rate models are well suited to the analysis of exposures or treatments in relation to individual failure time outcomes, when failure time dependencies are themselves of little or no interest. On the other hand semiparametric copula models are well suited to analyses where interest focuses primarily on the magnitude of dependencies between failure times. These models overlap with frailty models, that seem best suited to exploring the details of failure time clustering. Recently proposed multivariate marginal hazard methods, on the other hand, are well suited to the exploration of exposures or treatments in relation to single, pairwise, and higher dimensional hazard rates. Here these methods will be briefly described, and the final method will be illustrated using the Women’s Health Initiative hormone therapy trial data.

Keywords: Censoring; Copula; Dual outcomes hazard ratio; Failure times; Frailty; Hazard rate; Multivariate; Regression (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09558-4

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