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

Tingting Yu (), Lang Wu and Peter Gilbert
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Tingting Yu: University of British Columbia
Lang Wu: University of British Columbia
Peter Gilbert: University of Washington

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 2, No 3, 229-258

Abstract: 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.

Keywords: Lower limit of quantification; H-likelihood; Shared-parameter model; Mixed-effect model; Cox model (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10985-018-9434-7

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