Clinical risk prediction models and informative cluster size: Assessing the performance of a suicide risk prediction algorithm.

ABSTRACT: Clinical visit data are clustered within people, which complicates prediction modeling. Cluster size is often informative because people receiving more care are less healthy and at higher risk of poor outcomes. We used data from seven health systems on 1,518,968 outpatient mental health visits from January 1, 2012 to June 30, 2015 to predict suicide attempt within 90 days. We evaluated true performance of prediction models using a prospective validation set of 4,286,495 visits from October 1, 2015 to September 30, 2017. We examined dividing clustered data on the person or visit level for model training and cross-validation and considered a within cluster resampling approach for model estimation. We evaluated optimism by comparing estimated performance from a left-out testing dataset to per

SUBMITTER: Coley RY 

PROVIDER: S-EPMC9134927 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature