Improvement of an External Predictive Model Based on New Information Using a Synthetic Data Approach: Application to CADASIL.

Background and Objectives

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most frequent hereditary cerebral small vessel disease. It is caused by mutations of the

Methods

We used a new synthetic data approach to improve the initial predictive model by incorporating additional genetic information. This method combined the predicted outcomes from the previous model and 5 "synthetic" data sets with the observed outcome in a new data set. We then applied a multiple imputation method for missing data on the mutation location.

Results

The new data set included 367 patients who were followed up for 30 to 42 months. In the multivariable model with synthetic data, patients with

Discussion

The use of synthetic data improved the predictive model of MDRS change over 3 years in CADASIL. The predictive performance and estimation robustness of the predictive model were enhanced using this approach, whether genetic information was used. A statistically significant association between the location of the mutation in the

Informations de copyright

Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

Déclaration de conflit d'intérêts

The authors report no relevant disclosures. Go to Neurology.org/NG for full disclosures.