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. 2019;48(12):2982-3004.
doi: 10.1080/03610926.2018.1473599. Epub 2018 Sep 19.

A Likelihood Based Approach for Joint Modeling of Longitudinal Trajectories and Informative Censoring Process

Miran A Jaffa  1 , Ayad A Jaffa  2   3
Affiliations

A Likelihood Based Approach for Joint Modeling of Longitudinal Trajectories and Informative Censoring Process

Miran A Jaffa et al. Commun Stat Theory Methods. 2019.

Abstract

We propose a joint modeling likelihood-based approach for studies with repeated measures and informative right censoring. Joint modeling of longitudinal and survival data are common approaches but could result in biased estimates if proportionality of hazards is violated. To overcome this issue, and given that the exact time of dropout is typically unknown, we modeled the censoring time as the number of follow-up visits and extended it to be dependent on selected covariates. Longitudinal trajectories for each subject were modeled to provide insight into disease progression and incorporated with the number follow-up visits in one likelihood function.

Keywords: Biomarkers of kidney disease; Informative right censoring; Joint modeling; Latent random variables; Likelihood-based approach; Longitudinal data; Maximum likelihood estimation; Shared random effects.

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Conflict of interest statement

The authors have no conflict of interest to declare and all authors read and approved the paper.

Figures

Figure 1
Figure 1
Individual and mean values of predicted empirical Bayes estimates for individual intercepts of logarithmic plasma prekallikrein plotted against the number of last visits adjusted for SBP, Lipids and treatment effect. Mean values are represented by the diamond symbol.
Figure 2
Figure 2
Individual and mean values of predicted empirical Bayes estimates for individual slopes of logarithmic plasma prekallikrein plotted against the number of last visits adjusted for SBP, Lipids and treatment effect. Mean values are represented by the diamond symbol.
See this image and copyright information in PMC

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