A Correlated Binary Model for Ignorable Missing Data: Application to Rheumatoid Arthritis Clinical Data

Incomplete data are common phenomenon in research that adopts the longitudinal design approach. If incomplete observations are present in the longitudinal data structure, ignoring it could lead to bias in statistical inference and interpretation. We adopt the disposition model and extend it to the a...

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Bibliographic Details
Published in:Journal of Data Science 2016-04, Vol.14 (2), p.365-382
Main Authors: Erebholo, Francis, Apprey, Victor, Bezandry, Paul, Kwagyan, John
Format: Article
Language:English
Online Access:Get full text
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Summary:Incomplete data are common phenomenon in research that adopts the longitudinal design approach. If incomplete observations are present in the longitudinal data structure, ignoring it could lead to bias in statistical inference and interpretation. We adopt the disposition model and extend it to the analysis of longitudinal binary outcomes in the presence of monotone incomplete data. The response variable is modeled using a conditional logistic regression model. The nonresponse mechanism is assumed ignorable and developed as a combination of Markov’s transition and logistic regression model. MLE method is used for parameter estimation. Application of our approach to rheumatoid arthritis clinical trials is presented.
ISSN:1683-8602
1680-743X
1683-8602
DOI:10.6339/JDS.201604_14(2).0009