A different perspective on studying stroke predictors: joint models for longitudinal and time-to-event data in a type 2 diabetes mellitus cohort.

ABSTRACT:

Background

Most predictive models rely on risk factors and clinical outcomes assessed simultaneously. This approach does not adequately reflect the progression of health conditions. By employing joint models of longitudinal and survival data, we can dynamically adjust prognosis predictions for individual patients. Our objective was to optimize the prediction of stroke or transient ischemic attack (TIA) via joint models that incorporate all available changes in the predictive variables.

Methods

A total of 3442 patients with type 2 diabetes mellitus (T2DM) and no history of stroke, TIA or myocardial infarction were followed for 12 years. Models were constructed independently for men and women. We used proportional hazards regression models to assess the effects of baseline cha

SUBMITTER: Andres-Rebollo FJS 

PROVIDER: S-EPMC12004838 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature