Protective estimation of longitudinal categorical data with nonrandom dropout

Abstract

Partially observed longitudinal categorical data, where the partial classification arises due to monotone dropout, are analyzed using a protective estimator, which was first suggested by Brown (Biometrics, 1990) for normally distributed data. It is appropriate when dropout depends on the unobserved outcomes only, a particular type of nonignorable nonresponse. Estimation of measurement parameters is possible, without explicitly modelling the dropout process. Necessary and sufficient conditions are derived in order to have a unique solution in the interior of the parameter space. It is shown that precision estimates can be based on the delta method, the EM algorithm, and on multiple imputation. The relative merits of these techniques are discussed and they are contrasted with direct likelihood estimation and with pseudo-likelihood estimation. The method is illustrated using data taken from a psychiatric study.