Resumo

AYALA, YOLIMA  e  MELO, ÓSCAR ORLANDO. Estimation of Missing Data in Repeated Measurements with Binary Response. Rev.Colomb.Estad. [online]. 2007, vol.30, n.2, pp.265-285. ISSN 0120-1751.

A maximum likelihood method is proposed to provide estimates for models with binary response in longitudinal data based on an univariate model. Under a missing at random (MAR) mechanism, the EM algorithm is used in two different forms: in the first, the E step can be expressed as a weighted log-likelihood responses given the previous times, based in the method of weights proposed by Ibrahim (1990), for partially missing covariates. In the second, on the E step the estimation and imputation for missing data is based in Ancova method proposed by Bartlett (1937). Finally, we apply our method to the data from the Muscatine Coronary Risk Factor Study, employed in Fitzmaurice et al. (1994).

Palavras-chave : Longitudinal data; Logistic regression; Maximum likelihood; EM algorithm.

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