A0984
Title: Improved semiparametric estimation of the proportional rate model with recurrent event data
Authors: Ming-Yueh Huang - Academia Sinica (Taiwan) [presenting]
ChiungYu Huang - University of California, San Francisco (United States)
Abstract: The pseudo-partial likelihood method, known for its robustness, marginal interpretations, and ease of implementation, has become the default method for analyzing recurrent event data using Cox-type proportional rate models, as introduced in previous seminal papers. However, the pseudo-partial score function's construction does not account for dependency among recurrent events, leading to potential inefficiency. The asymptotic efficiency of weighted pseudo-partial likelihood estimation is explored, demonstrating that the optimal weight function depends on the unknown variance-covariance process of the recurrent event process and may lack a closed-form expression. Therefore, combining a set of pre-specified weighted pseudo-partial score equations is proposed using the generalized method of moments and empirical likelihood estimation rather than determining optimal weights. The findings indicate that significant efficiency improvements can be readily achieved without introducing additional model assumptions. Furthermore, the proposed estimation methods can be executed using existing software. Both theoretical and numerical analyses reveal that the empirical likelihood estimator is more desirable than the generalized method of moments estimator when the sample size is sufficiently large.
