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Random effects and latent processes approaches for analyzing binary longitudinal data with missingness: a comparison of approaches using opiate clinical trial data

Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, USA, albertp{at}mail.nih.gov

Dean A. Follmann

Biostatistics Research Branch, National Institute of Allergy and Infectious Disease, USA

The analysis of longitudinal data with non-ignorable missingness remains an active area in biostatistics research. This article discusses various random effects and latent process models which have been proposed for analyzing longitudinal binary data subject to both non-ignorable intermittent missing data and dropout. These models account for non-ignorable missingness by introducing random effects or a latent process which is shared between the response model and the model for the missing-data mechanism. We discuss various random effects and latent processes approaches and compare these approaches with analyses from an opiate clinical trial data set, which had high proportion of intermittent missingness and dropout. We also compare these random effect and latent process approaches with other methods for accounting for non-ignorable missingness using this data set.

This version was published on October 1, 2007

Statistical Methods in Medical Research, Vol. 16, No. 5, 417-439 (2007)
DOI: 10.1177/0962280206075308


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