JSM 2004 - Toronto

Abstract #301008

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Activity Number: 273
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301008
Title: A Method for Imputing Missing Correlated Binary Responses
Author(s): Sandra Hall*+ and Jasjit Ahluwalia and Matthew Mayo
Companies: University of Kansas Medical Center and University of Kansas Medical Center and University of Kansas Medical Center
Address: 3901 Rainbow Blvd. MS1008, Kansas City, KS, 66160,
Keywords: longitudinal ; generalized linear mixed model
Abstract:

Missing data is a common problem in medical research. Many methods have been used to handle missing data at a single time point. However, a number of studies in medical research are longitudinal, resulting in correlated outcomes at numerous time points. We are imputing missing correlated binary data arising from a longitudinal study. We propose a method based on generalized linear mixed model to impute missing data and compare it to a traditional imputation strategy using commercially available software. These two methods are compared and contrasted, and recommendations are made. Each method is then applied to a dataset from a smoking cessation intervention study. The response was a binary correlated outcome, in that it is the repeated measurement of whether the subject had quit smoking or not over time.

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