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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. "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 1-43, May.
    • Handle: RePEc:spr:testjl:v:18:y:2009:i:1:p:1-43
    DOI: 10.1007/s11749-009-0138-x
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    References listed on IDEAS

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    1. Hong‐Tu Zhu & Sik‐Yum Lee, 2001. "Local influence for incomplete data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 111-126.
    2. Garrett M. Fitzmaurice & Stuart R. Lipsitz & Geert Molenberghs & Joseph G. Ibrahim, 2001. "Bias in Estimating Association Parameters for Longitudinal Binary Responses with Drop‐Outs," Biometrics, The International Biometric Society, vol. 57(1), pages 15-21, March.
    3. Lan Huang & Ming-Hui Chen & Joseph G. Ibrahim, 2005. "Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates," Biometrics, The International Biometric Society, vol. 61(3), pages 767-780, September.
    4. Xiaoyan Shi & Hongtu Zhu & Joseph G. Ibrahim, 2009. "Local Influence for Generalized Linear Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1164-1174, December.
    5. Jane Xu & Scott L. Zeger, 2001. "Joint analysis of longitudinal data comprising repeated measures and times to events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 375-387.
    6. Elizabeth R. Brown & Joseph G. Ibrahim & Victor DeGruttola, 2005. "A Flexible B-Spline Model for Multiple Longitudinal Biomarkers and Survival," Biometrics, The International Biometric Society, vol. 61(1), pages 64-73, March.
    7. Herring A. H & Ibrahim J. G, 2001. "Likelihood-Based Methods for Missing Covariates in the Cox Proportional Hazards Model," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 292-302, March.
    8. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    9. 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.
    10. Chen, Qingxia & Ibrahim, Joseph G. & Chen, Ming-Hui & Senchaudhuri, Pralay, 2008. "Theory and inference for regression models with missing responses and covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1302-1331, July.
    11. Qingxia Chen & Joseph G. Ibrahim, 2006. "Semiparametric Models for Missing Covariate and Response Data in Regression Models," Biometrics, The International Biometric Society, vol. 62(1), pages 177-184, March.
    12. Amy H. Herring & Joseph G. Ibrahim & Stuart R. Lipsitz, 2002. "Frailty Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 58(1), pages 98-109, March.
    13. 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.
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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. 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).
    5. 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.
    6. 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).
    7. 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.
    8. Zhou, Jing & Lan, Wei & Wang, Hansheng, 2022. "Asymptotic covariance estimation by Gaussian random perturbation," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.

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