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

Noémie Soullier,Jean Bouyer,Elise De La Rochebrochard

doi:10.1186/1471-2288-10-79

Abstract

BackgroundIn 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.MethodsWe compared the performance of multiple imputation with that of Kaplan-Meier estimation in several simulated attrition scenarios.ResultsIn 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%).ConclusionsMultiple 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.

Highlights

In longitudinal cohort studies, subjects may be lost to follow-up at any time during the study
The KaplanMeier approach tends to overestimate the in vitro fertilization (IVF) success rate, and alternative methods have recently been proposed: drop-outs are divided into two groups depending on the chances of success [12], or, equivalently, according to whether IVF treatment is interrupted for medical causes or not [13]
It focuses on the estimation of a 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

Summary

Introduction

Subjects may be lost to follow-up at any time during the study. Survival analysis and Kaplan-Meier estimation are commonly used to examine time-to-event measurements [5,6] This approach takes into account the fact that subjects are followed for different lengths of time. It assumes that censored patients (including patients lost to followup) would have the same probability of experiencing a subsequent event as non-censored patients. Couples with a poor chance of success will be considered as having a zero probability of subsequent success, whereas those with a good prognosis will have the same chance of subsequent success as those who persevere This method makes it possible to take into account auxiliary information on the probability of success among drop-outs, through individual prognostic factors, considered in two groups. Multiple imputation is a good candidate approach in this setting

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