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- W77330898 abstract "It is well known that in the complete longitudinal setup, the so-called working correlation-based generalized estimating equations (GEE) approach may yield less efficient regression estimates as compared to the independence assumption-based method of moments and quasi-likelihood (QL) estimates. In the incomplete longitudinal setup, there exist some studies indicating that the use of the same “working” correlation-based GEE approach may provide inconsistent regression estimates especially when the longitudinal responses are at risk of being missing at random (MAR). In this paper, we revisit this inconsistency issue under a longitudinal binary model and empirically examine the relative performance of the existing weighted (by inverse probability weights for the missing indicator) GEE (WGEE), a fully standardized GQL (FSGQL) and conditional GQL (CGQL) approaches. In the comparative study, we consider both stationary and non-stationary covariates, as well as various degrees of missingness and longitudinal correlation in the data." @default.
- W77330898 created "2016-06-24" @default.
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- W77330898 date "2013-01-01" @default.
- W77330898 modified "2023-09-26" @default.
- W77330898 title "Consistent Estimation in Incomplete Longitudinal Binary Models" @default.
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- W77330898 doi "https://doi.org/10.1007/978-1-4614-6871-4_6" @default.
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