Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 48, Issue 9, Pages 2812-2829Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2018.1468457
Keywords
Dropout missingness; inverse probability weight; generalized estimating equations; missing at random; model selection; quasi-likelihood; R
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Funding
- NCATS NIH HHS [UL1 TR002014, KL2 TR002015, KL2 TR000126] Funding Source: Medline
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Missing data arise frequently in clinical and epidemiological fields, in particular in longitudinal studies. This paper describes the core features of an R package wgeesel, which implements marginal model fitting (i.e., weighted generalized estimating equations, WGEE; doubly robust GEE) for longitudinal data with dropouts under the assumption of missing at random. More importantly, this package comprehensively provide existing information criteria for WGEE model selection on marginal mean or correlation structures. Also, it can serve as a valuable tool for simulating longitudinal data with missing outcomes. Lastly, a real data example and simulations are presented to illustrate and validate our package.
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