Personalized feature threshold estimation in joint modelling of longitudinal and time-to-event data

TL;DR

This paper introduces methods to estimate personalized risk factor thresholds in joint models of longitudinal and time-to-event data, accounting for individual characteristics like sex and race, to improve CVD risk assessment.

Contribution

The paper develops novel methods for estimating individualized risk thresholds in joint models, allowing thresholds to vary by sex, race, and feature type, enhancing personalized CVD risk prediction.

Findings

Thresholds vary significantly across sex and race groups.

Personalized thresholds improve risk stratification accuracy.

Methods applied to ARIC Study data demonstrate practical utility.

Abstract

Cardiovascular disease (CVD) cohort studies collect longitudinal data on numerous CVD risk factors including body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), glucose, and total cholesterol. The commonly used threshold values for identifying subjects at high risk are 30 kg/$m^2$ for BMI, 120 mmHg for SBP, 80 mmHg for DBP, 126 mg/dL for glucose, and 230 mg/dL for total cholesterol. When studying the association between features of longitudinal risk factors and time to a CVD event, an important research question is whether these CVD risk factor thresholds should vary based on individual characteristics as well as the type of longitudinal feature being considered. Using data from the Atherosclerosis Risk in Communities (ARIC) Study, we develop methods to estimate risk factor thresholds in joint models with multiple features for each longitudinal risk factor. These thresholds are allowed to vary by sex, race, and type of feature. Our methods have the potential for personalized CVD prevention strategies as well as better estimates of CVD risk.