A Markov model for longitudinal studies with incomplete dichotomous outcomes

Orestis Efthimiou, Nicky J Welton, Myrto Samara, Stefan Leucht, Georgia Salanti, GetReal Work Package 4

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)
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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 multi-state 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 two 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.
Original languageEnglish
Pages (from-to)122–132
Number of pages11
JournalPharmaceutical Statistics
Volume16
Issue number2
Early online date5 Dec 2016
DOIs
Publication statusPublished - Mar 2017

Structured keywords

  • ConDuCT-II

Keywords

  • Bayesian analysis
  • missing data
  • multi-state models

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