title: Doubly robust estimation, optimally truncated inverse-intensity weighting and increment-based methods for the analysis of irregularly observed longitudinal data.
abstract: 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.
authors.affiliation: Biostatistics Unit, St Joseph's Healthcare, 50 Charlton Ave E, Canada. pullena@mcmaster.ca
authors.forename: Eleanor M
authors.initials: EM
authors.lastname: Pullenayegum
authors.forename: Brian M
authors.initials: BM
authors.lastname: Feldman
mesh_terms: D019540:Area Under Curve; D002648:Child; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003882:Dermatomyositis / Q000473:pathology; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D013269:Stochastic Processes