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Abstract

Various non-proportional hazard models have been developed in the literature for competing risks data. The regression coefficients under these models, however, typically cannot be compared directly. We propose new methods to quantify the average of the time-varying cause-specific hazard ratios and subdistribution hazard ratios through two general classes of transformations and weight functions that are chosen to reflect the relative importance of the hazard ratios in different time periods. We further propose an $L_{\infty}$ -norm type of test statistic that incorporates the test statistics for all possible pairs of the transformation function and weight function under consideration. Extensive simulations are conducted under various settings of the hazards and demonstrate that the proposed test performs well under all settings. An application to a clinical trial in follicular lymphoma is examined in detail.

Keywords

Cause-specific hazards Cox proportional hazard model estimand subdistribution hazards time-varying hazard ratios weighted estimation

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Quantifying time-varying cause-specific hazard and subdistribution hazard ratios with competing risks data

Abstract

Keywords

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