IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v18y2009i1p1-43.html
   My bibliography  Save this article

Missing data methods in longitudinal studies: a review

Author

Listed:
  • Joseph Ibrahim
  • Geert Molenberghs

Abstract

No abstract is available for this item.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11749-009-0138-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11749-009-0138-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Ming-Hui & Ibrahim, Joseph G. & Shao, Qi-Man, 2009. "Maximum likelihood inference for the Cox regression model with applications to missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2018-2030, October.
    2. Fang, Fang & Shao, Jun, 2016. "Iterated imputation estimation for generalized linear models with missing response and covariate values," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 111-123.
    3. Chen, Xue-Dong & Fu, Ying-Zi, 2011. "Model selection for zero-inflated regression with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 765-773, January.
    4. Baojiang Chen & Xiao-Hua Zhou, 2011. "Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 830-842, September.
    5. Manuel Galea & Patricia Giménez, 2019. "Local influence diagnostics for the test of mean–variance efficiency and systematic risks in the capital asset pricing model," Statistical Papers, Springer, vol. 60(1), pages 293-312, February.
    6. 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.
    7. Bindele, Huybrechts F. & Nguelifack, Brice M., 2019. "Generalized signed-rank estimation for regression models with non-ignorable missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 14-33.
    8. Nanhua Zhang & Roderick J. Little, 2012. "A Pseudo-Bayesian Shrinkage Approach to Regression with Missing Covariates," Biometrics, The International Biometric Society, vol. 68(3), pages 933-942, September.
    9. Hongtu Zhu & Joseph G. Ibrahim & Yueh-Yun Chi & Niansheng Tang, 2012. "Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 954-964, September.
    10. repec:jss:jstsof:35:i09 is not listed on IDEAS
    11. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
    12. Giménez, Patricia & Galea, Manuel, 2013. "Influence measures on corrected score estimators in functional heteroscedastic measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 1-15.
    13. Alejandra Tapia & Victor Leiva & Maria del Pilar Diaz & Viviana Giampaoli, 2019. "Influence diagnostics in mixed effects logistic regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 920-942, September.
    14. Pang, W. K. & Yang, Z. H. & Hou, S. H. & Leung, P. K., 2002. "Non-uniform random variate generation by the vertical strip method," European Journal of Operational Research, Elsevier, vol. 142(3), pages 595-609, November.
    15. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    16. Z. Rezaei Ghahroodi & M. Ganjali, 2013. "A Bayesian approach for analysing longitudinal nominal outcomes using random coefficients transitional generalized logit model: an application to the labour force survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(7), pages 1425-1445, July.
    17. Anis Fradi & Chafik Samir & Ines Adouani, 2024. "A New Bayesian Approach to Global Optimization on Parametrized Surfaces in $$\mathbb {R}^{3}$$ R 3," Journal of Optimization Theory and Applications, Springer, vol. 202(3), pages 1077-1100, September.
    18. Malay Naskar & Kalyan Das & Joseph G. Ibrahim, 2005. "A Semiparametric Mixture Model for Analyzing Clustered Competing Risks Data," Biometrics, The International Biometric Society, vol. 61(3), pages 729-737, September.
    19. Antonello Loddo & Shawn Ni & Dongchu Sun, 2011. "Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 342-355, July.
    20. Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous–discrete covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 803-825, June.
    21. Nandram, Balgobin & Zelterman, Daniel, 2007. "Computational Bayesian inference for estimating the size of a finite population," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2934-2945, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:testjl:v:18:y:2009:i:1:p:1-43. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.