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Joint partially linear model for longitudinal data with informative drop-outs

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  • Sehee Kim
  • Donglin Zeng
  • Jeremy M. G. Taylor

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  • 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.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:72-82
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    File URL: http://hdl.handle.net/10.1111/biom.12566
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    References listed on IDEAS

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    1. Caroline Beunckens & Geert Molenberghs & Geert Verbeke & Craig Mallinckrodt, 2008. "A Latent-Class Mixture Model for Incomplete Longitudinal Gaussian Data," Biometrics, The International Biometric Society, vol. 64(1), pages 96-105, March.
    2. Jason Roy & Xihong Lin, 2005. "Missing Covariates in Longitudinal Data with Informative Dropouts: Bias Analysis and Inference," Biometrics, The International Biometric Society, vol. 61(3), pages 837-846, September.
    3. Jianhua Z. Huang, 2002. "Varying-coefficient models and basis function approximations for the analysis of repeated measurements," Biometrika, Biometrika Trust, vol. 89(1), pages 111-128, March.
    4. Na Cai & Wenbin Lu & Hao Helen Zhang, 2012. "Time-Varying Latent Effect Model for Longitudinal Data with Informative Observation Times," Biometrics, The International Biometric Society, vol. 68(4), pages 1093-1102, December.
    5. Dimitris Rizopoulos & Geert Verbeke & Geert Molenberghs, 2010. "Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes," Biometrics, The International Biometric Society, vol. 66(1), pages 20-29, March.
    6. Jimin Ding & Jane-Ling Wang, 2008. "Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data," Biometrics, The International Biometric Society, vol. 64(2), pages 546-556, June.
    7. Jason Roy, 2003. "Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model," Biometrics, The International Biometric Society, vol. 59(4), pages 829-836, December.
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    Cited by:

    1. Janet Niekerk & Haakon Bakka & Håvard Rue, 2023. "Stable Non-Linear Generalized Bayesian Joint Models for Survival-Longitudinal Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 102-128, February.
    2. Andrew Atkinson & Suzie Cro & James R. Carpenter & Michael G. Kenward, 2021. "Information anchored reference‐based sensitivity analysis for truncated normal data with application to survival analysis," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(4), pages 500-523, November.

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