Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command
Eric J. Daza
Stanford Prevention Research Center
Stanford University
Stanford, CA
[email protected]
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Michael G. Hudgens
Department of Biostatistics
University of North Carolina at Chapel Hill
Chapel Hill, NC
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Amy H. Herring
Department of Biostatistics and Carolina Population Center
University of North Carolina at Chapel Hill
Chapel Hill, NC
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Abstract. Individuals may drop out of a longitudinal study, rendering their outcomes
unobserved but still well defined. However, they may also undergo truncation
(for example, death), beyond which their outcomes are no longer meaningful.
Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a
method to conduct regression conditioning on nontruncation, that is, regression
conditioning on continuation (RCC), for longitudinal outcomes that are
monotonically missing at random (for example, because of dropout). This method
first estimates the probability of dropout among continuing individuals to
construct inverse-probability weights (IPWs), then fits generalized estimating
equations (GEE) with these IPWs. In this article, we present the
xtrccipw command, which can both estimate the IPWs required by RCC and
then use these IPWs in a GEE estimator by calling the glm command from
within xtrccipw. In the absence of truncation, the xtrccipw
command can also be used to run a weighted GEE analysis. We demonstrate the
xtrccipw command by analyzing an example dataset and the original
Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate
some empirical properties of RCC through a simulation study.
View all articles by these authors:
Eric J. Daza, Michael G. Hudgens, Amy H. Herring
View all articles with these keywords:
xtrccipw, dropout, generalized estimating equations, inverse-probability weights, longitudinal data, missing at random, truncation, weighted GEE
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