English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 in simulations of clusters with varying calibration slopes.    Results  The  c-mbc  was less extreme in distribution than the c-index in 19 European centers (internal validation;  n  = 1716) and 36 non-European centers (external validation;  n  = 3135) of the CRASH trial. In simulations, the  c-mbc  was biased but less variable than the naïve c-index, resulting in lower root mean squared errors.    Conclusions  The  c-mbc , based on multilevel regression analysis of the calibration slope, is an attractive alternative to the c-index as a measure of discriminative ability in multicenter studies with patient clusters of limited sample size.    Electronic supplementary material  The online version of this article (10.1186/s41512-019-0055-8) contains supplementary material, which is available to authorized users.

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