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New approaches for censored longitudinal data in joint modelling of longitudinal and survival data, with application to HIV vaccine studies

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  • Tingting Yu

    (University of British Columbia)

  • Lang Wu

    (University of British Columbia)

  • Peter Gilbert

    (University of Washington
    Fred Hutchinson Cancer Research Center)

Abstract

In HIV vaccine studies, longitudinal immune response biomarker data are often left-censored due to lower limits of quantification of the employed immunological assays. The censoring information is important for predicting HIV infection, the failure event of interest. We propose two approaches to addressing left censoring in longitudinal data: one that makes no distributional assumptions for the censored data—treating left censored values as a “point mass” subgroup—and the other makes a distributional assumption for a subset of the censored data but not for the remaining subset. We develop these two approaches to handling censoring for joint modelling of longitudinal and survival data via a Cox proportional hazards model fit by h-likelihood. We evaluate the new methods via simulation and analyze an HIV vaccine trial data set, finding that longitudinal characteristics of the immune response biomarkers are highly associated with the risk of HIV infection.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:2:d:10.1007_s10985-018-9434-7
    DOI: 10.1007/s10985-018-9434-7
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    References listed on IDEAS

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    1. 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.
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    3. Wendelin Schnedler, 2005. "Likelihood Estimation for Censored Random Vectors," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 195-217.
    4. Commenges, D. & Jolly, D. & Drylewicz, J. & Putter, H. & Thiébaut, R., 2011. "Inference in HIV dynamics models via hierarchical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 446-456, January.
    5. Wu L., 2002. "A Joint Model for Nonlinear Mixed-Effects Models With Censoring and Covariates Measured With Error, With Application to AIDS Studies," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 955-964, December.
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