Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma.

ABSTRACT:

Background

Scleroderma is a serious chronic autoimmune disease in which a patient's disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems. Threshold crossings of pulmonary and cardiac measures indicate potentially life-threatening key clinical events including interstitial lung disease (ILD), cardiomyopathy, and pulmonary hypertension (PH). The statistical challenge is to accurately and precisely predict these events by using all of the clinical history for the patient at hand and for a reference population of patients.

Methods

We use a Bayesian mixed model approach to simultaneously characterize each individual's future trajectories for several biomarkers. We estimate this model using a large population of

SUBMITTER: Kim JS 

PROVIDER: S-EPMC8590788 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature