A Markov chain model for longitudinal categorical data when there may be non-ignorable non-response

Longitudinal data with non-response occur in studies where the same subject is followed over time but data for each subject may not be available at every time point. When the response is categorical and the response at time t depends on the response at the previous time points, it may be appropriate...

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Bibliographic Details
Published inJournal of applied statistics Vol. 26; no. 1; pp. 5 - 18
Main Authors Huang, Saling, Brown, Morton B.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis Group 01.01.1999
Taylor and Francis Journals
Taylor & Francis Ltd
SeriesJournal of Applied Statistics
Subjects
Algorithms
Estimates
Longitudinal studies
Markov analysis
Missing data
Ratios
Simulation
Statistical analysis
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ISSN0266-4763
1360-0532
DOI10.1080/02664769922610

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Summary:Longitudinal data with non-response occur in studies where the same subject is followed over time but data for each subject may not be available at every time point. When the response is categorical and the response at time t depends on the response at the previous time points, it may be appropriate to model the response using a Markov model. We generalize a second-order Markov model to include a non-ignorable non-response mechanism. Simulation is used to study the properties of the estimators. Large sample sizes are necessary to ensure that the algorithm converges and that the asymptotic properties of the estimators can be used.
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ISSN:0266-4763
1360-0532
DOI:10.1080/02664769922610