Semiparametric regression and risk prediction with competing risks data under missing cause of failure

Giorgos Bakoyannis (), Ying Zhang and Constantin T. Yiannoutsos
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Giorgos Bakoyannis: Indiana University Fairbanks School of Public Health and School of Medicine
Ying Zhang: University of Nebraska Medical Center
Constantin T. Yiannoutsos: Indiana University Fairbanks School of Public Health and School of Medicine

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 4, No 2, 659-684

Abstract: Abstract The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. To address this, several methods have been proposed for the semiparametric proportional cause-specific hazards model under a missing at random assumption. However, these proposals provide inference for the regression coefficients only, and do not consider the infinite dimensional parameters, such as the covariate-specific cumulative incidence function. Nevertheless, the latter quantity is essential for risk prediction in modern medicine. In this paper we propose a unified framework for inference about both the regression coefficients of the proportional cause-specific hazards model and the covariate-specific cumulative incidence functions under missing at random cause of failure. Our approach is based on a novel computationally efficient maximum pseudo-partial-likelihood estimation method for the semiparametric proportional cause-specific hazards model. Using modern empirical process theory we derive the asymptotic properties of the proposed estimators for the regression coefficients and the covariate-specific cumulative incidence functions, and provide methodology for constructing simultaneous confidence bands for the latter. Simulation studies show that our estimators perform well even in the presence of a large fraction of missing cause of failures, and that the regression coefficient estimator can be substantially more efficient compared to the previously proposed augmented inverse probability weighting estimator. The method is applied using data from an HIV cohort study and a bladder cancer clinical trial.

Keywords: Cause-specific hazard; Cumulative incidence function; Confidence band; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-020-09494-1

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