Α Markov model for longitudinal studies with incomplete dichotomous outcomes.

Efthimiou, Orestis; Welton, Nicky; Samara, Myrto; Leucht, Stefan; Salanti, Georgia (2017). Α Markov model for longitudinal studies with incomplete dichotomous outcomes. Pharmaceutical Statistics, 16(2), pp. 122-132. Wiley 10.1002/pst.1794

[img]
Preview
Text
Efthimiou PharmStat 2017.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (560kB) | Preview

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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

UniBE Contributor:

Efthimiou, Orestis and Salanti, Georgia

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

1539-1604

Publisher:

Wiley

Language:

English

Submitter:

Doris Kopp Heim

Date Deposited:

07 Dec 2016 21:09

Last Modified:

25 Oct 2019 07:07

Publisher DOI:

10.1002/pst.1794

PubMed ID:

27917593

Uncontrolled Keywords:

Bayesian analysis; missing data; multistate models

BORIS DOI:

10.7892/boris.91200

URI:

https://boris.unibe.ch/id/eprint/91200

Actions (login required)

Edit item Edit item
Provide Feedback