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.
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Acknowledgements
Research reported in this publication was supported by the National Institute Of Allergy And Infectious Diseases (NIAID) of the National Institutes of Health (NIH) under Award Numbers R37AI054165 and UM1AI068635, and by the Bill and Melinda Gates Foundation Award Number OPP1110049. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or BMGF. The authors thank the participants, investigators, and sponsors of the VAX004, including Global Solutions for Infectious Diseases.
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Yu, T., Wu, L. & Gilbert, P. New approaches for censored longitudinal data in joint modelling of longitudinal and survival data, with application to HIV vaccine studies. Lifetime Data Anal 25, 229–258 (2019). https://doi.org/10.1007/s10985-018-9434-7
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DOI: https://doi.org/10.1007/s10985-018-9434-7