Multiple outputation for the analysis of longitudinal data subject to irregular observation.
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.
['Pullenayegum|Eleanor M|EM|']
D018906:Antineoplastic Agents, Alkylating D001499:Bayes Theorem D002648:Child D003198:Computer Simulation D003882:Dermatomyositis D006801:Humans D016014:Linear Models D008137:Longitudinal Studies D015233:Models, Statistical D064887:Observational Studies as Topic D016032:Randomized Controlled Trials as Topic D013269:Stochastic Processes D013852:Thiotepa D013997:Time Factors D001749:Urinary Bladder Neoplasms
longitudinal data inverse-intensity weighting joint models