Missing Covariates in Longitudinal Data with Informative Dropouts: Bias Analysis and Inference
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- Amy L. Stubbendick & Joseph G. Ibrahim, 2003. "Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects Models," Biometrics, The International Biometric Society, vol. 59(4), pages 1140-1150, December.
- Geert Verbeke & Geert Molenberghs & Herbert Thijs & Emmanuel Lesaffre & Michael G. Kenward, 2001. "Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach," Biometrics, The International Biometric Society, vol. 57(1), pages 7-14, March.
- J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
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Cited by:
- Samiran Sinha & Tapabrata Maiti, 2008. "Analysis of Matched Case–Control Data in Presence of Nonignorable Missing Exposure," Biometrics, The International Biometric Society, vol. 64(1), pages 106-114, March.
- Weiping Zhang & Feiyue Xie & Jiaxin Tan, 2020. "A robust joint modeling approach for longitudinal data with informative dropouts," Computational Statistics, Springer, vol. 35(4), pages 1759-1783, December.
- Wenqin Pan & Donglin Zeng & Xihong Lin, 2009. "Estimation in Semiparametric Transition Measurement Error Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 65(3), pages 728-736, September.
- Sehee Kim & Donglin Zeng & Jeremy M. G. Taylor, 2017. "Joint partially linear model for longitudinal data with informative drop-outs," Biometrics, The International Biometric Society, vol. 73(1), pages 72-82, March.
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