Zoom: https://harvard.zoom.us/j/95208130086?pwd=em0ycE95SzBFYU5tNXFnREZ2c2tHQT09, Password: 722888
Presenter: Kaitlyn Cook, PhD ('20), Postdoctoral Research Fellow, Department of Population Medicine, Harvard Medical School
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 research questions, we propose a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates model estimation without requiring strict parametric assumptions. We also introduce a robust, profile composite likelihood-based estimator for the corresponding model variance. In this presentation, we will characterize the finite-sample performance of these estimators through extensive simulation studies. We will then apply these estimators 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.