The calibrated model-based concordance improved assessment of discriminative ability in patient clusters of limited sample size.

ABSTRACT:

Background

Discriminative ability is an important aspect of prediction model performance, but challenging to assess in clustered (e.g., multicenter) data. Concordance (c)-indexes may be too extreme within small clusters. We aimed to define a new approach for the assessment of discriminative ability in clustered data.

Methods

We assessed discriminative ability of a prediction model for the binary outcome mortality after traumatic brain injury within centers of the CRASH trial. With multilevel logistic regression analysis, we estimated cluster-specific calibration slopes which we used to obtain the recently proposed calibrated model-based concordance (c-mbc) within each cluster. We compared the c-mbc with the naïve c-index in centers of the CRASH trial and

SUBMITTER: van Klaveren D 

PROVIDER: S-EPMC6551913 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature