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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Dichotomizing continuous outcome variables is a common procedure in medical sciences. When analyzing these variables using binary logistic regression, great attention should be paid to the choice of the measure of explained variation (. Since there are many different  R2  in logistic regression, in order to make correct inferences about models, evaluating their performances has become more important. The purpose of this paper is to reveal asymptotically more efficient and reliable  R2  measure when analyzing the models with dichotomized outcome. The eight most recommended  R2  statistics and ordinary least squares  R2  associated with the underlying continuous outcome have been included. Their asymptotic distributions have been studied. They have also been compared under varying correlational conditions between outcome and covariate. Extensive simulations using the bootstrap method have been conducted under two modeling scenarios. A real data example is also presented. The findings provide support and important basis for making efficient decisions.

communications in statistics: case studies, data analysis and applications

Effects of dichotomizing continuous outcome on efficiencies of measures of explained variation in logistic regression: Simulation study and application