Fast Accelerated Failure Time Modeling for Case-Cohort Data

TL;DR

This paper introduces a fast, efficient method for fitting accelerated failure time models to case-cohort data, overcoming computational challenges and enabling practical application in medical studies.

Contribution

It develops a new smoothing-based estimation approach and variance estimation technique tailored for AFT models with case-cohort data, improving speed and validity.

Findings

Proposed method is faster than existing approaches.

Simulation shows accurate inference with the new method.

Application demonstrates practical utility in tumor study.

Abstract

Semiparametric accelerated failure time (AFT) models directly relate the predicted failure times to covariates and are a useful alternative to models that work on the hazard function or the survival function. For case-cohort data, much less development has been done with AFT models. In addition to the missing covariates outside of the sub-cohort in controls, challenges from AFT model inferences with full cohort are retained. The regression parameter estimator is hard to compute because the most widely used rank-based estimating equations are not smooth. Further, its variance depends on the unspecified error distribution, and most methods rely on computationally intensive bootstrap to estimate it. We propose fast rank-based inference procedures for AFT models, applying recent methodological advances to the context of case-cohort data. Parameters are estimated with an induced smoothing approach that smooths the estimating functions and facilitates the numerical solution. Variance estimators are obtained through efficient resampling methods for nonsmooth estimating functions that avoids full blown bootstrap. Simulation studies suggest that the recommended procedure provides fast and valid inferences among several competing procedures. Application to a tumor study demonstrates the utility of the proposed method in routine data analysis.