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Competing risks and multivariate outcomes in epidemiological and clinical trial research

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Abstract

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women’s Health Initiative cohort and clinical trial data sets, and additional research needs will be described.

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Acknowledgements

This work was partially supported by National Institute of Health grant and National Heart, Lung, and Blood Institute contract.

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  1. Fred Hutchinson Cancer Center, 1100 Fairview Ave N., Seattle, WA, 98109, USA

    R. L. Prentice

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Prentice, R.L. Competing risks and multivariate outcomes in epidemiological and clinical trial research. Lifetime Data Anal (2024). https://doi.org/10.1007/s10985-024-09629-8

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