title: Α Markov model for longitudinal studies with incomplete dichotomous outcomes.
abstract: Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. The time of each observation is accounted for, and the model allows the estimation of time-dependent relative treatment effects. We apply our methods to data from a study comparing the effectiveness of 2 pharmacological treatments for schizophrenia. The model jointly estimates the relative efficacy and the dropout rate and also allows for a wide range of clinically interesting inferences to be made. Assumptions about the missingness mechanism and the unobserved outcomes of patients dropping out can be incorporated into the analysis. The presented method constitutes a viable candidate for analyzing longitudinal, incomplete binary data.
authors.affiliation: Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
authors.forename: Orestis
authors.initials: O
authors.lastname: Efthimiou
authors.affiliation: School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors.forename: Nicky
authors.initials: N
authors.lastname: Welton
authors.affiliation: Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany.
authors.forename: Myrto
authors.initials: M
authors.lastname: Samara
authors.affiliation: Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Leucht
authors.affiliation: Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.|Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
authors.forename: Georgia
authors.initials: G
authors.lastname: Salanti
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D012559:Schizophrenia / Q000188:drug therapy; D013997:Time Factors
keywords: Bayesian analysis; missing data; multistate models