| Summary: | <p>Abstract</p> <p>Background</p> <p>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 <it>P(E) </it>of an event <it>E</it>, when the first occurrence of this event is observed at <it>t </it>successive time points of a longitudinal study with attrition.</p> <p>Methods</p> <p>We compared the performance of multiple imputation with that of Kaplan-Meier estimation in several simulated attrition scenarios.</p> <p>Results</p> <p>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%).</p> <p>Conclusions</p> <p>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.</p>
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