Marginal Regression on Transient State Occupation Probabilities with Clustered Multistate Process Data
Abstract
Clustered multistate process data are commonly encountered in multicenter observational studies and clinical trials. A clinically important estimand with such data is the marginal probability of being in a particular transient state as a function of time. However, there is currently no method for nonparametric marginal regression analysis of these probabilities with clustered multistate process data. To address this problem, we propose a weighted functional generalized estimating equations approach which does not impose Markov assumptions or assumptions regarding the structure of the within-cluster dependence, and allows for informative cluster size (ICS). The asymptotic properties of the proposed estimators for the functional regression coefficients are rigorously established and a nonparametric hypothesis testing procedure for covariate effects is proposed. Simulation studies show that the proposed method performs well even with a small number of clusters, and that ignoring the within-cluster dependence and the ICS leads to invalid inferences. The proposed method is used to analyze data from a multicenter clinical trial on recurrent or metastatic squamous-cell carcinoma of the head and neck with a stratified randomization design.
Cite
@article{arxiv.2209.00804,
title = {Marginal Regression on Transient State Occupation Probabilities with Clustered Multistate Process Data},
author = {Wenxian Zhou and Giorgos Bakoyannis and Ying Zhang and Constantin T Yiannoutsos},
journal= {arXiv preprint arXiv:2209.00804},
year = {2022}
}