Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases.

ABSTRACT:

Background

Confounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database.

Methods

Data from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) oper

SUBMITTER: Chandran U 

PROVIDER: S-EPMC6919633 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature