title: 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.
authors.affiliation: Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, ON, Canada.|Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
authors.forename: Eleanor M
authors.initials: EM
authors.lastname: Pullenayegum
mesh_terms: D018906:Antineoplastic Agents, Alkylating / Q000627:therapeutic use; D001499:Bayes Theorem; D002648:Child; D003198:Computer Simulation; D003882:Dermatomyositis / Q000473:pathology; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D064887:Observational Studies as Topic / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic; D013269:Stochastic Processes; D013852:Thiotepa / Q000627:therapeutic use; D013997:Time Factors; D001749:Urinary Bladder Neoplasms / Q000188:drug therapy
chemical_list: D018906:Antineoplastic Agents, Alkylating; D013852:Thiotepa
keywords: inverse-intensity weighting; joint models; longitudinal data