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Mixed Effects Models with Censored Data with Application to HIV RNA Levels

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  • James P. Hughes

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  • James P. Hughes, 1999. "Mixed Effects Models with Censored Data with Application to HIV RNA Levels," Biometrics, The International Biometric Society, vol. 55(2), pages 625-629, June.
    • Handle: RePEc:bla:biomet:v:55:y:1999:i:2:p:625-629
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.1999.00625.x
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    Cited by:

    1. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.
    2. Dagne Getachew & Huang Yangxin, 2012. "Bayesian inference for a nonlinear mixed-effects Tobit model with multivariate skew-t distributions: application to AIDS studies," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-24, September.
    3. Wolter, Marcus & Rösch, Daniel, 2014. "Cure events in default prediction," European Journal of Operational Research, Elsevier, vol. 238(3), pages 846-857.
    4. Kalyan Das & Angshuman Sarkar, 2014. "Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(11), pages 2418-2436, November.
    5. Matos, Larissa A. & Lachos, Victor H. & Balakrishnan, N. & Labra, Filidor V., 2013. "Influence diagnostics in linear and nonlinear mixed-effects models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 450-464.
    6. 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.
    7. Wan-Lun Wang & Tsung-I Lin, 2022. "Robust clustering of multiply censored data via mixtures of t factor analyzers," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 22-53, March.
    8. Huiyun Wu & Qingxia Chen & Lorraine B. Ware & Tatsuki Koyama, 2012. "A Bayesian approach for generalized linear models with explanatory biomarker measurement variables subject to detection limit: an application to acute lung injury," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(8), pages 1733-1747, March.
    9. Choi, Taehwa & Kim, Arlene K.H. & Choi, Sangbum, 2021. "Semiparametric least-squares regression with doubly-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    10. Robert H. Lyles & Jovonne K. Williams & Rutt Chuachoowong, 2001. "Correlating Two Viral Load Assays with Known Detection Limits," Biometrics, The International Biometric Society, vol. 57(4), pages 1238-1244, December.
    11. Yaakov Malinovsky & Paul S. Albert & Enrique F. Schisterman, 2012. "Pooling Designs for Outcomes under a Gaussian Random Effects Model," Biometrics, The International Biometric Society, vol. 68(1), pages 45-52, March.
    12. Marc Lavielle & Adeline Samson & Ana Karina Fermin & France Mentré, 2011. "Maximum Likelihood Estimation of Long-Term HIV Dynamic Models and Antiviral Response," Biometrics, The International Biometric Society, vol. 67(1), pages 250-259, March.
    13. Vaida, Florin & Fitzgerald, Anthony P. & DeGruttola, Victor, 2007. "Efficient hybrid EM for linear and nonlinear mixed effects models with censored response," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5718-5730, August.
    14. Samson, Adeline & Lavielle, Marc & Mentre, France, 2006. "Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1562-1574, December.
    15. Paul S. Albert, 2008. "Modeling Longitudinal Biomarker Data from Multiple Assays that Have Different Known Detection Limits," Biometrics, The International Biometric Society, vol. 64(2), pages 527-537, June.
    16. Francisco H. C. Alencar & Larissa A Matos & Víctor H. Lachos, 2022. "Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 463-486, November.
    17. Victor H. Lachos & Dipankar Bandyopadhyay & Dipak K. Dey, 2011. "Linear and Nonlinear Mixed-Effects Models for Censored HIV Viral Loads Using Normal/Independent Distributions," Biometrics, The International Biometric Society, vol. 67(4), pages 1594-1604, December.
    18. Tao Lu, 2017. "Bayesian inference on longitudinal-survival data with multiple features," Computational Statistics, Springer, vol. 32(3), pages 845-866, September.
    19. Tingting Yu & Lang Wu & Peter Gilbert, 2019. "New approaches for censored longitudinal data in joint modelling of longitudinal and survival data, with application to HIV vaccine studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 229-258, April.
    20. Larissa A. Matos & Luis M. Castro & Víctor H. Lachos, 2016. "Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(4), pages 627-653, December.
    21. Hongbin Zhang & Lang Wu, 2018. "A non‐linear model for censored and mismeasured time varying covariates in survival models, with applications in human immunodeficiency virus and acquired immune deficiency syndrome studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1437-1450, November.

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