Doubly robust estimation, optimally truncated inverse‐intensity weighting and increment‐based methods for the analysis of irregularly observed longitudinal data. (10th October 2012)

• Record Type: Journal Article
• Authors: Pullenayegum, Eleanor M.; Feldman, Brian M.
• Is Part Of: Statistics in medicine. Volume 32:Number 6(2013)
• Page Start: 1054
• Page End: 1072
• Publication Date: 2012-10-10
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.5640
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim5640-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim5640-para-0001">Longitudinal data arising from routine follow‐up of patients will often have irregular measurement times. Existing methods for analysis include joint modelling of the outcome and measurement processes, and inverse‐intensity weighting (IIW). This work extends previously proposed analysis of increments to the case of irregular follow‐up, yielding a model for the increments that can be used as a stand‐alone method. Furthermore, we propose two ways of combining the increments and IIW estimators. First, we use the increment model to select the truncation point for the inverse‐intensity weights that minimises the mean squared error of the IIW estimator. Second, we use the increment model to augment the usual IIW estimating equations to form a doubly robust estimator. We evaluate the methods through simulation and apply these to a recent study of juvenile dermatomyositis. Copyright © 2012 John Wiley &amp; Sons, Ltd.</p> </abstract>