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Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model

Margaret C. Wu and Kent R. Bailey
Biometrics
Vol. 45, No. 3 (Sep., 1989), pp. 939-955
DOI: 10.2307/2531694
Stable URL: http://www.jstor.org/stable/2531694
Page Count: 17
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
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Abstract

A general linear regression model for the usual least squares estimated rate of change (slope) on censoring time is described as an approximation to account for informative right censoring in estimating and comparing changes of a continuous variable in two groups. Two noniterative estimators for the group slope means, the linear minimum variance unbiased (LMVUB) estimator and the linear minimum mean squared error (LMMSE) estimator, are proposed under this conditional model. In realistic situations, we illustrate that the LMVUB and LMMSE estimators, derived under a simple linear regression model, are quite competitive compared to the pseudo maximum likelihood estimator (PMLE) derived by modeling the censoring probabilities. Generalizations to polynomial response curves and general linear models are also described.

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