A Markov model for longitudinal studies with incomplete dichotomous outcomes

作者：Efthimiou Orestis^*; Welton Nicky; Samara Myrto; Leucht Stefan; Salanti Georgia

来源：Pharmaceutical Statistics, 2017, 16(2): 122-132.

DOI：10.1002/pst.1794

• Bayesian analysis
• missing data
• multistate models

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 timedependent 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.

• 出版日期2017-4