title: A Correlated Binary Model for Ignorable Missing Data: Application to Rheumatoid Arthritis Clinical Data.
abstract: 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.
authors.affiliation: Department of Mathematics, Hampton University.
authors.forename: Francis
authors.initials: F
authors.lastname: Erebholo
authors.affiliation: National Human Genome Center, Howard University.
authors.forename: Victor
authors.initials: V
authors.lastname: Apprey
authors.affiliation: Department of Mathematics, Howard University.
authors.forename: Paul
authors.initials: P
authors.lastname: Bezandry
authors.affiliation: National Human Genome Center, Howard University; Department of Community and Family Medicine, Howard University.
authors.forename: John
authors.initials: J
authors.lastname: Kwagyan
keywords: Binary data; disposition model; dropout mechanism; ignorable missingness; maximum likelihood estimation