Titlebook: Logistic Regression with Missing Values in the Covariates; Werner Vach Book 1994 Springer-Verlag New York, Inc. 1994 Conditional probabili

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Missing Values and Subsamplingple is planned in advance, the missing value mechanism satisfies the MAR assumption. We consider in this chapter the application of ML estimation, semiparametric ML estimation and estimation of the score function in this setting. Modifications of these approaches are necessary, if the subsample is a

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Further Examplesirst example the application of ML estimation allows a substantial gain in the precision relative to a Complete Case Analysis, but doubts of the validity of the MAR assumption require a sensitivity analysis. In the second example a Complete Case Analysis gives a somewhat dubious result and we have t

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General Regression Models with Missing Values in One of Two Covariatess have been made. We refer to the excellent review of Little (1992). Note, however, that many of the methods described there depend on the assumption of a joint multivariate normal distribution of all variables.

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Discussion) methods with good and some (old) methods with bad statistical properties. The presented difficulties to give general recommendations also highlight some important issues of future research. Additional topics are summarized in the third section, and the book finishes with an important remark.

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Missing Values and Subsampling validation sample with precise measurements of a covariate, for which a surrogate covariate is measured for all subjects. Finally we consider subsampling of the nonresponders and the sampling of additional variables to avoid a violation of the MAR assumption.