Multiple imputation for estimation of an occurrence rate in cohorts with attrition and discrete follow-up time points: a simulation study.
BACKGROUND
In longitudinal cohort studies, subjects may be lost to follow-up at any time during the study. This leads to attrition and thus to a risk of inaccurate and biased estimations. The purpose of this paper is to show how multiple imputation can take advantage of all the information collected during follow-up in order to estimate the cumulative probability P(E) of an event E, when the first occurrence of this event is observed at t successive time points of a longitudinal study with attrition.


METHODS
We compared the performance of multiple imputation with that of Kaplan-Meier estimation in several simulated attrition scenarios.


RESULTS
In missing-completely-at-random scenarios, the multiple imputation and Kaplan-Meier methods performed well in terms of bias (less than 1%) and coverage rate (range = [94.4%; 95.8%]). In missing-at-random scenarios, the Kaplan-Meier method was associated with a bias ranging from -5.1% to 7.0% and with a very poor coverage rate (as low as 0.2%). Multiple imputation performed much better in this situation (bias <2%, coverage rate >83.4%).


CONCLUSIONS
Multiple imputation shows promise for estimation of an occurrence rate in cohorts with attrition. This study is a first step towards defining appropriate use of multiple imputation in longitudinal studies.
Soullier
Noémie
N
INSERM, CESP Centre for research in Epidemiology and Population Health, U1018 Reproduction and Child Development Team, F-94276 Le Kremlin-Bicêtre, France.
de La Rochebrochard
Elise
E
Bouyer
Jean
J
D000328:Adult; D001530:Belgium; D006302:Health Services Research; D006801:Humans; D007407:Interviews as Topic; D008875:Middle Aged; D010821:Physicians, Family / Q000523:psychology*; D019338:Polypharmacy; D012424:Rural Population; D014505:Urban Population
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