| Literature DB >> 32346220 |
Cong Xu1, Zheng Li2, Yuan Xue3, Lijun Zhang4, Ming Wang2.
Abstract
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.Entities:
Keywords: Dropout missingness; R; generalized estimating equations; inverse probability weight; missing at random; model selection; quasi-likelihood
Year: 2018 PMID: 32346220 PMCID: PMC7188076 DOI: 10.1080/03610918.2018.1468457
Source DB: PubMed Journal: Commun Stat Simul Comput ISSN: 0361-0918 Impact factor: 1.118