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Activity Number: 352
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306428
Title: Multilevel Modeling of Follow-Up Studies with Missing Data
Author(s): Monica Bennett*+ and James Stamey and John W. Seaman, PhD
Companies: Baylor Health Care System and Baylor University and Baylor University
Address: 2323 N. Field St, Dallas, TX, 75201, United States
Keywords: Multilevel models ; missing data ; Poisson Regression ; Bayesian Analysis

Missing data is often an unavoidable problem. Here we are particularly concerned with missing data in follow-up studies aimed to estimate the rate of incidence of a particular event, such as death or injury, among different groups. The observed counts are usually assumed to follow a Poisson distribution, but the analysis is complicated when some of the records indicating the cause of the incident have been destroyed or lost. If we ignore the missing data then both the overall event rates and the uncertainty in the model parameters will be underestimated. To help overcome this problem, we develop a Bayesian random intercepts and slopes Poisson model to account for the missing data. By using a multilevel model we can better determine the effect that several different factors have on the outcome. The model is developed so that it estimates the rates of incidence, as well as the probability of missing information. We also discuss how model selection tools can be used to determine the most appropriate model for the data.

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