Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or
an event in the future course of disease (prognosis) for individual patients. Although …

Background How to select variables and identify functional forms for continuous variables is
a key concern when creating a multivariable model. Ad hoc 'traditional'approaches to …

Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or
prognosis in healthcare. Hundreds of prediction models are published in the medical …

When designing a study to develop a new prediction model with binary or time‐to‐event
outcomes, researchers should ensure their sample size is adequate in terms of the number …

Binary logistic regression is one of the most frequently applied statistical approaches for
developing clinical prediction models. Developers of such models often rely on an Events …

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the …

Aims We hypothesized that the modified Diamond–Forrester (DF) prediction model
overestimates probability of coronary artery disease (CAD). The aim of this study was to …

Objectives To develop a risk prediction model to preoperatively discriminate between
benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian …

In the medical literature, hundreds of prediction models are being developed to predict
health outcomes in individuals. For continuous outcomes, typically a linear regression model …

The performance of prediction models can be assessed using a variety of methods and
metrics. Traditional measures for binary and survival outcomes include the Brier score to …