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- W3168262015 abstract "Missing data occurs commonly in longitudinal studies. When data are missing at random, full-likelihood approaches may be used for analysis. However, when the missing observations are nonignorable and nonmonotone, a full-likelihood approach may yield inconsistent estimates. To correct for any bias that may arise in longitudinal studies with nonignorable missing data, various modeling strategies have been introduced, including selection, pattern-mixture, and shared-parameter models. However, because these classes of joint models explicitly model the nonignorable missingness mechanism, they require strong and unverifiable assumptions to guarantee model identifiability. To avoid this, the protective estimator has been proposed as an approach to analyze longitudinal data subject to nonignorable, nonmonotone missingness. The protective estimator avoids estimating the nonignorable missing data by utilizing a key assumption: the probability that a response is missing at any instance depends on the value of the variable alone in an unspecified way. In this article, we introduce the protective estimator and present its mathematical framework. We elaborate on advantages and disadvantages of the modeling approach and review examples of the protective estimator used in practice across the literature." @default.
- W3168262015 created "2021-06-22" @default.
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- W3168262015 date "2018-11-13" @default.
- W3168262015 modified "2023-09-27" @default.
- W3168262015 title "The Protective Estimator: A Tool for Longitudinal Analysis with Missing Data" @default.
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- W3168262015 doi "https://doi.org/10.1002/9781118445112.stat08139" @default.
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