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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Pullenayegum EMdoi
10.1002/sim.6829subject
Has Abstractpub_date
2016-05-20 00:00:00pages
1800-18issue
11eissn
0277-6715issn
1097-0258journal_volume
35pub_type
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