Application of multivariate joint modeling of longitudinal biomarkers and time-to-event data to a rare kidney stone cohort.

ABSTRACT:

Background

Time-dependent Cox proportional hazards regression is a popular statistical method used in kidney disease research to evaluate associations between biomarkers collected serially over time with progression to kidney failure. Typically, biomarkers of interest are considered time-dependent covariates being updated at each new measurement using last observation carried forward (LOCF). Recently, joint modeling has emerged as a flexible alternative for multivariate longitudinal and time-to-event data. This study describes and demonstrates multivariate joint modeling using as an example the association of serial biomarkers (plasma oxalate [POX] and urinary oxalate [UOX]) and kidney function among patients with primary hyperoxaluria in the Rare Kidney Stone Consortium Registry.<

SUBMITTER: Vaughan LE 

PROVIDER: S-EPMC9879888 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature