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.
Adult
Algorithms
Antibody Formation
/ drug effects
Female
Graft Rejection
/ immunology
Humans
Immunosuppressive Agents
/ administration & dosage
Kidney Transplantation
/ methods
Male
Markov Chains
Middle Aged
Models, Theoretical
Monte Carlo Method
Tacrolimus
/ administration & dosage
Time Factors
Tissue Donors
Bayesian analysis
Interval censoring
Measurement error
Shared random effects
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
26 06 2019
26 06 2019
Historique:
received:
11
02
2019
accepted:
09
06
2019
entrez:
28
6
2019
pubmed:
28
6
2019
medline:
7
7
2020
Statut:
epublish
Résumé
Tacrolimus (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. Using 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). The 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. To avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.
Sections du résumé
BACKGROUND
Tacrolimus (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.
METHODS
Using 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).
RESULTS
The 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.
CONCLUSIONS
To avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.
Identifiants
pubmed: 31242848
doi: 10.1186/s12874-019-0773-1
pii: 10.1186/s12874-019-0773-1
pmc: PMC6595621
doi:
Substances chimiques
Immunosuppressive Agents
0
Tacrolimus
WM0HAQ4WNM
Types de publication
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
130Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR002535
Pays : United States
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