Predicting urinary stone recurrence: a joint model analysis of repeated 24-hour urine collections from the MSTONE database.

ABSTRACT: To address the limitations in existing urinary stone recurrence (USR) models, including failure to account for changes in 24-hour urine (24U) parameters over time and ignoring multiplicity of stone recurrences, we presented a novel statistical method to jointly model temporal trends in 24U parameters and multiple recurrent stone events. The MSTONE database spanning May 2001 to April 2015 was analyzed. A joint recurrent model was employed, combining a linear mixed-effects model for longitudinal 24U parameters and a recurrent event model with a dynamic first-order Autoregressive (AR(1)) structure. A mixture cure component was included to handle patient heterogeneity. Comparisons were made with existing methods, multivariable Cox regression and conditional Prentice-Williams-Peterson regressio

SUBMITTER: Kong Z 

PROVIDER: S-EPMC11530469 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature