Sample size calculation to externally validate scoring systems based on logistic regression models.

ABSTRACT:

Background

A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model.

Objective

The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.

Methods

The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of ca

SUBMITTER: Palazon-Bru A 

PROVIDER: S-EPMC5411086 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature