Login / Signup

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.

Kristen R CampbellElizabeth Juarez-ColungaGary K GrunwaldJames CooperScott DavisJane Gralla
Published in: BMC medical research methodology (2019)
To avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.
Keyphrases
  • kidney transplantation
  • big data
  • machine learning
  • adipose tissue
  • skeletal muscle
  • deep learning
  • weight loss
  • glycemic control