Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data

Lola Etievant; Mitchell H. Gail

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arXiv · 2304.03396

Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data

Lola Etievant·Mitchell H. Gail

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Abstract

The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used "robust" variance estimate of Barlow is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.

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Lola Etievant, Mitchell H. Gail. 2023-04-06. Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data. https://arxiv.org/abs/2304.03396

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