ArticleBMC medical research methodology2019
Comparison of a time-varying covariate model and a joint model of time-to-event outcomes in the presence of measurement error and interval censoring: application to kidney transplantation.
Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.Statistical methods in medical research · 2026Article
- Machine learning prediction of tuberculosis mortality: a comparative analysis of random survival forest and cox regression models.BMC infectious diseases · 2026Article
- Dynamic mortality predictions from serum albumin in dialysis patients using robust joint models with competing risks.Scientific reports · 2025Article
- A multivariate joint model to adjust for random measurement error while handling skewness and correlation in dietary data in an epidemiologic study of mortality.Annals of epidemiology · 2023Article
- Using joint models to study the association between CD4 count and the risk of death in TB/HIV data.BMC medical research methodology · 2022Article
- Development and validation of a dynamic survival prediction model for patients with acute-on-chronic liver failure.JHEP reports : innovation in hepatology · 2021Article
- Joint modeling of liver transplant candidates outperforms the model for end-stage liver disease: The effect of disease development over time on patient outcome.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2021Article
- Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.BMC medical research methodology · 2021Article
- Dynamic prediction based on variability of a longitudinal biomarker.BMC medical research methodology · 2021Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTacrolimus (TAC) is an immunosuppressant drug given to kidney transplant recipients post-transplant to prevent antibody formation and kidney rejection. The optimal therapeutic dose for TAC is poorly defined and therapy requires frequent monitoring of drug trough levels. Analyzing the association between TAC levels over time and the development of potentially harmful de novo donor specific antibodies (dnDSA) is complex because TAC levels are subject to measurement error and dnDSA is assessed at discrete times, so it is an interval censored time-to-event outcome.
methodsUsing data from the University of Colorado Transplant Center, we investigated the association between TAC and dnDSA using a shared random effects (intercept and slope) model with longitudinal and interval censored survival sub-models (JM) and compared it with the more traditional interval censored survival model with a time-varying covariate (TVC). We carried out simulations to compare bias, level and power for the association parameter in the TVC and JM under varying conditions of measurement error and interval censoring. In addition, using Markov Chain Monte Carlo (MCMC) methods allowed us to calculate clinically relevant quantities along with credible intervals (CrI).
resultsThe shared random effects model was a better fit and showed both the average TAC and the slope of TAC were associated with risk of dnDSA. The simulation studies demonstrated that, in the presence of heavy interval censoring and high measurement error, the TVC survival model underestimates the association between the survival and longitudinal measurement and has inflated type I error and considerably less power to detect associations.
conclusionsTo avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.
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