Semiparametric marginal promotion time cure model for clustered survival data

TL;DR

This paper introduces a semiparametric marginal promotion time cure model for clustered survival data, providing two efficient estimation methods that outperform existing approaches in accuracy and efficiency, with applications to periodontal disease data.

Contribution

The paper develops two novel estimation methods for a semiparametric cure model in clustered survival data, demonstrating their superior efficiency and consistency.

Findings

Both methods are more efficient than existing ones regardless of correlation structure.

Quadratic inference functions provide higher efficiency than generalized estimating equations.

Application reveals new insights into periodontal disease data.

Abstract

Modeling clustered/correlated failure time data has been becoming increasingly important in clinical trials and epidemiology studies. In this paper, we consider a semiparametric marginal promotion time cure model for clustered right-censored survival data with a cure fraction. We propose two estimation methods based on the generalized estimating equations and the quadratic inference functions and prove that the regression estimates from the two proposed methods are consistent and asymptotic normal and that the estimates from the quadratic inference functions are optimal. The simulation study shows that the estimates from both methods are more efficient than those from the existing method no matter whether the correlation structure is correctly specified. The estimates based on the quadratic inference functions achieve higher efficiency compared with those based on the generalized estimating equations under the same working correlation structure. An application of the proposed methods is demonstrated with periodontal disease data and new findings are revealed in the analysis.