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Activity Number: 404 - Quantile, Semiparametric and Nonparametric Methods in Survival Analysis
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #306390 Presentation 1 Presentation 2
Title: Stochastic Expectation Maximization for Semiparametric Regression Analysis of Multivariate Interval-Censored Data
Author(s): Kaitlyn Cook* and Rui Wang
Companies: Harvard University and Harvard University
Keywords: Cluster-randomized trial; HIV; Interval censoring; Mixed models

Multivariate interval-censored data often arise in cluster-randomized trials where the outcome of interest is an asymptomatic event. For example, in cluster-randomized HIV prevention studies, the presence of an infection is determined through periodic serological testing, producing correlated and interval-censored observations. One natural choice for modeling these data is the mixed-effects proportional hazards model. However, most current algorithms for fitting these models to interval-censored data require that cluster sizes are small relative to the number of clusters---an assumption that often fails to hold in practice. Here we present a stochastic expectation maximization algorithm that permits semiparametric estimation of mixed-effects proportional hazards models with interval-censored data, even in settings where cluster membership is large. We also introduce a perturbation-resampling scheme to estimate the covariance matrix of the resulting estimators. Finally, we demonstrate the performance of our method using data modeled on the Botswana Combination Prevention Project, a large cluster-randomized trial of combination HIV prevention.

Authors who are presenting talks have a * after their name.

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