A likelihood based approach for joint modeling of longitudinal trajectories and informative censoring process

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

Acknowledgments

We would like to acknowledge the DCCT/EDIC as the source of data. A complete list of participants in the DCCT/EDIC Research Group is presented in the Supplementary Material published online for the article in N Engl J Med 2015;372:1722–33.

Funding

This work was supported by the National Institutes of Health Grants HL077192 (AAJ), and 5 P01 HL055782.