Developing risk models for multicenter data using standard logistic regression produced suboptimal predictions: A simulation study.

ABSTRACT: Although multicenter data are common, many prediction model studies ignore this during model development. The objective of this study is to evaluate the predictive performance of regression methods for developing clinical risk prediction models using multicenter data, and provide guidelines for practice. We compared the predictive performance of standard logistic regression, generalized estimating equations, random intercept logistic regression, and fixed effects logistic regression. First, we presented a case study on the diagnosis of ovarian cancer. Subsequently, a simulation study investigated the performance of the different models as a function of the amount of clustering, development sample size, distribution of center-specific intercepts, the presence of a center-predictor interacti

SUBMITTER: Falconieri N 

PROVIDER: S-EPMC7383814 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature