Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command

Eric J. Daza (), Michael G. Hudgens and Amy H. Herring
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Eric J. Daza: Stanford University
Michael G. Hudgens: University of North Carolina at Chapel Hill
Amy H. Herring: University of North Carolina at Chapel Hill

Stata Journal, 2017, vol. 17, issue 2, 253-278

Abstract: Individuals may drop out of a longitudinal study, rendering their out- comes unobserved but still well defined. However, they may also undergo trunca- tion (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to con- duct 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 prob- ability 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 call- ing 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 demon- strate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some em- pirical properties of RCC through a simulation study. Copyright 2017 by StataCorp LP.

Keywords: xtrccipw; dropout; generalized estimating equations; inverse-probability weights; longitudinal data; missing at random; truncation; weighted GEE (search for similar items in EconPapers)
Date: 2017
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