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Rejoinder on: Missing data methods in longitudinal studies: a review

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  • Joseph Ibrahim
  • Geert Molenberghs

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  • Joseph Ibrahim & Geert Molenberghs, 2009. "Rejoinder on: Missing data methods in longitudinal studies: a review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 68-75, May.
    • Handle: RePEc:spr:testjl:v:18:y:2009:i:1:p:68-75
    DOI: 10.1007/s11749-009-0144-z
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    References listed on IDEAS

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    1. Jansen, Ivy & Hens, Niel & Molenberghs, Geert & Aerts, Marc & Verbeke, Geert & Kenward, Michael G., 2006. "The nature of sensitivity in monotone missing not at random models," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 830-858, February.
    2. Ibrahim, Joseph G. & Zhu, Hongtu & Tang, Niansheng, 2008. "Model Selection Criteria for Missing-Data Problems Using the EM Algorithm," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1648-1658.
    3. Ming-Hui Chen & Joseph G. Ibrahim & Qi-Man Shao, 2006. "Posterior propriety and computation for the Cox regression model with applications to missing covariates," Biometrika, Biometrika Trust, vol. 93(4), pages 791-807, December.
    4. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    5. Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
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    Citations

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    Cited by:

    1. Nanhua Zhang & Henian Chen & Yuanshu Zou, 2014. "A joint model of binary and longitudinal data with non-ignorable missingness, with application to marital stress and late-life major depression in women," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 1028-1039, May.
    2. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    3. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
    4. Francesco Bravo, 2020. "Robust estimation and inference for general varying coefficient models with missing observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 966-988, December.
    5. Li, Chao & Zhang, Yuhan & Li, Xiang & Hao, Yanwei, 2024. "Artificial intelligence, household financial fragility and energy resources consumption: Impacts of digital disruption from a demand-based perspective," Resources Policy, Elsevier, vol. 88(C).
    6. Li, Chao & Sun, Daoming, 2023. "Women’s bargaining power and spending on children’s education: Evidence from a natural experiment in China," International Journal of Educational Development, Elsevier, vol. 100(C).
    7. Maria Gheorghe & Susan Picavet & Monique Verschuren & Werner B. F. Brouwer & Pieter H. M. Baal, 2017. "Health losses at the end of life: a Bayesian mixed beta regression approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 723-749, June.
    8. Daniel O. Scharfstein & Jon Steingrimsson & Aidan McDermott & Chenguang Wang & Souvik Ray & Aimee Campbell & Edward Nunes & Abigail Matthews, 2022. "Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders," Biometrics, The International Biometric Society, vol. 78(2), pages 649-659, June.
    9. Antonello Maruotti, 2015. "Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 84-109, March.
    10. Cai, T. Tony & Zhang, Anru, 2016. "Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 55-74.
    11. Tithi Biswas & Kylie H. Kang & Rohin Gawdi & David Bajor & Mitchell Machtay & Charu Jindal & Jimmy T. Efird, 2020. "Using the Systemic Immune-Inflammation Index (SII) as a Mid-Treatment Marker for Survival among Patients with Stage-III Locally Advanced Non-Small Cell Lung Cancer (NSCLC)," IJERPH, MDPI, vol. 17(21), pages 1-13, October.
    12. Li, Xiaofei & Huebner, E. Scott & Tian, Lili, 2021. "Vicious cycle of emotional maltreatment and bullying perpetration/victimization among early adolescents: Depressive symptoms as a mediator," Social Science & Medicine, Elsevier, vol. 291(C).
    13. 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.
    14. An-Min Tang & Nian-Sheng Tang & Dalei Yu, 2023. "Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 888-918, October.
    15. D. Claire Miller & Samantha MaWhinney & Jennifer L. Patnaik & Karen L. Christopher & Anne M. Lynch & Brandie D. Wagner, 2022. "Predictors of refraction prediction error after cataract surgery: a shared parameter model to account for missing post-operative measurements," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 343-364, June.
    16. Zhou, Jing & Lan, Wei & Wang, Hansheng, 2022. "Asymptotic covariance estimation by Gaussian random perturbation," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).

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