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Analyzing longitudinal data in the presence of informative drop-out: The jmre1 command

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  • Nikos Pantazis

    (University of Athens Medical School)

  • Giota Touloumi

    (University of Athens Medical School)

Abstract

Many studies in various research areas have designs that involve repeated measurements over time of a continuous variable across a group of subjects. A frequent and serious problem in such studies is the occurrence of missing data. In many cases, missing data are caused by an event that leads to a premature termination of the series of repeated measurements on some subjects. When the probability of the occurrence of this event is related to the subject-specific under- lying trend of the variable of interest, this missingness process is called informative censoring or informative drop-out. Standard likelihood-based methods (for example, linear mixed models) fail to give consistent estimates. In such cases, one needs to apply methods that simultaneously model the observed data and the missingness process. In this article, we review a method proposed by Touloumi et al. (1999, Statistics in Medicine 18: 1215-1233) to adjust for informative drop-out in longitudinal data analysis. We also present the jmre1 command, which can be used to fit the proposed model. The estimation method combines the restricted it- erative generalized least-squares method with a nested expectation-maximization algorithm. The method is implemented mainly using Stata’s matrix programming language, Mata. Our example is derived from the epidemiology of the HIV infection. Copyright 2010 by StataCorp LP.

Suggested Citation

  • Nikos Pantazis & Giota Touloumi, 2010. "Analyzing longitudinal data in the presence of informative drop-out: The jmre1 command," Stata Journal, StataCorp LP, vol. 10(2), pages 226-251, June.
    • Handle: RePEc:tsj:stataj:v:10:y:2010:i:2:p:226-251
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    References listed on IDEAS

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    1. Goldstein, Harvey & Rasbash, Jon, 1992. "Efficient computational procedures for the estimation of parameters in multilevel models based on iterative generalised least squares," Computational Statistics & Data Analysis, Elsevier, vol. 13(1), pages 63-71, January.
    2. Sophia Rabe-Hesketh, 2002. "Multilevel selection models using gllamm," Dutch-German Stata Users' Group Meetings 2002 1, Stata Users Group.
    3. N. Pantazis & G. Touloumi & A. S. Walker & A. G. Babiker, 2005. "Bivariate modelling of longitudinal measurements of two human immunodeficiency type 1 disease progression markers in the presence of informative drop‐outs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(2), pages 405-423, April.
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    Cited by:

    1. Michael J. Crowther & Keith R. Abrams & Paul C. Lambert, 2013. "Joint modeling of longitudinal and survival data," Stata Journal, StataCorp LP, vol. 13(1), pages 165-184, March.

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