Multiple imputation for estimation of an occurrence rate in cohorts with attrition and discrete follow-up time points: a simulation study

BMC Med Res Methodol. 2010 Sep 3;10:79. doi: 10.1186/1471-2288-10-79.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Belgium
  • Health Services Research
  • Humans
  • Interviews as Topic
  • Middle Aged
  • Physicians, Family / psychology*
  • Polypharmacy*
  • Rural Population
  • Urban Population