An R package for model fitting,model selection and the simulation for longitudinal data with dropout missingness |
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| Authors: | Cong Xu Zheng Li Yuan Xue Lijun Zhang |
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| Affiliation: | 1. Vertex Pharmaceuticals, Boston, Massachusetts, USA;2. Department of Public Health Sciences, Division of Biostatistics and Bioinformatics, College of Medicine, Penn State Hershey Medical Center, Hershey, Pennsylvania, USA;3. School of Statistics, University of International Business and Economics, Beijing, China;4. Department of Biochemistry and Molecular Biology, Institute of Personalized Medicine, Penn State Hershey Medical Center, Hershey, Pennsylvania, USA |
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| Abstract: | AbstractMissing 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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| Keywords: | Dropout missingness inverse probability weight generalized estimating equations missing at random model selection quasi-likelihood R |
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