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

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, 6: 241-258) developed a method to conduct re...

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Veröffentlicht in:The Stata journal Jg. 17; H. 2; S. 253
Hauptverfasser: Daza, Eric J, Hudgens, Michael G, Herring, Amy H
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.06.2017
Schlagworte:
xtrccipw
weighted GEE
st0474
generalized estimating equations
dropout
inverse-probability weights
missing at random
truncation
longitudinal data
ISSN:1536-867X
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Zusammenfassung: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, 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.
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ISSN:1536-867X
DOI:10.1177/1536867X1701700202