Multiple outputation for the analysis of longitudinal data subject to irregular observation
Abstract
Observational cohort studies often feature longitudinal data subject to irregular observation. Moreover, the timings of observations may be associated with the underlying disease process and must thus be accounted for when analysing the data. This paper suggests that multiple outputation, which consists of repeatedly discarding excess observations, may be a helpful way of approaching the problem. Multiple outputation was designed for clustered data where observations within a cluster are exchangeable; an adaptation for longitudinal data subject to irregular observation is proposed. We show how multiple outputation can be used to expand the range of models that can be fitted to irregular longitudinal data. Copyright © 2015 John Wiley & Sons, Ltd.
Citing Literature
Number of times cited according to CrossRef: 2
- Eleanor M. Pullenayegum, Meeting the Assumptions of Inverse-Intensity Weighting for Longitudinal Data Subject to Irregular Follow-Up: Suggestions for the Design and Analysis of Clinic-Based Cohort Studies, Epidemiologic Methods, 10.1515/em-2018-0016, 0, 0, (2020).
- Michelle Greiver, Sumeet Kalia, Teja Voruganti, Babak Aliarzadeh, Rahim Moineddin, William Hinton, Martin Dawes, Frank Sullivan, Saddaf Syed, John Williams, Simon de Lusignan, Trends in end digit preference for blood pressure and associations with cardiovascular outcomes in Canadian and UK primary care: a retrospective observational study, BMJ Open, 10.1136/bmjopen-2018-024970, 9, 1, (e024970), (2019).
