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Activity Number: 375
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #320097
Title: Estimation and Modeling of Partnership Transition Probabilities and Concurrency Patterns: Recasting Duration Data in a Markov Chain and Logistic Regression Framework
Author(s): Yared Gurmu* and Nuala McGrath and Victor De Gruttola
Companies: and University of Southampton and Harvard
Keywords: EM ; Markov Chain ; Partnership data

We describe methods for modeling sexual partnership formation and termination using survey data collected both retrospectively and prospectively. Such methods are required to model propagation of sexually transmitted diseases and the impact of interventions on such processes-these models are being used both to design and to monitor HIV combination prevention studies. The modeling approach combine Markov and a logistic regression frameworks. The Markov model states we consider include celibacy, monogamy and concurrency; the logistic regression model classifies the pattern of concurrency, which can be either transitional (older partnership ends first) or embedded (new ends first). By using both types of models we can fully characterize the processes of interest. Estimation of model parameters is based on a stochastic expectation maximization algorithm (stEM) coupled with a rejection-sampling scheme. Strategies based on statistics that arise naturally from the estimation procedure itself are used to validate model assumptions. The method is illustrated using sexual history data from South Africa.

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

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