Author ORCID Identifier
Kaitlyn Cook: 0000-0003-3794-3312
Wenbin Lu: 0000-0002-7320-4755
Rui Wang: 0000-0001-5007-193X
Document Type
Article
Publication Date
10-31-2022
Publication Title
Biometrics
Volume
79
Issue
3
Abstract
The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana’s national adoption of a universal test-and-treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy (i) modified the observed preventative effects of the study intervention and (ii) was associated with a reduction in the population-level incidence of HIV in Botswana. To address these questions, we propose a stratified proportional hazards model for clustered intervalcensored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test-and-treat strategy now modeled as a time-dependent covariate.
First Page
1670
Last Page
1685
Recommended Citation
Cook, Kaitlyn; Lu, Wenbin; and Wang, Rui, "Marginal Proportional Hazards Models for Clustered Interval-Censored Data with Time-Dependent Covariates" (2022). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/80
Digital Object Identifier (DOI)
https://doi.org/10.1111/biom.13787
Rights
© The Authors
Included in
Data Science Commons, Other Computer Sciences Commons, Statistics and Probability Commons
Comments
Peer reviewed accepted manuscript.