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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/327

Title: Protective estimation of longitudinal categorical data with nonrandom dropout
Authors: Michiels, Bart
Molenberghs, Geert
Keywords: Missing data
Categorical data
Longitudinal data
Issue Date: 1997
Citation: Communications in Statistics Theory and Methods, 26(1). p. 65-94
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.
URI: http://hdl.handle.net/1942/327
DOI: 10.1080/03610929708831902
ISI #: A1997WF24700007
Type: Journal Contribution
Appears in Collections: Research publications

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