scispace - formally typeset
Open AccessBook

Clinical Prediction Models

Reads0
Chats0
TLDR
This paper presents a case study on survival analysis: Prediction of secondary cardiovascular events and lessons from case studies on overfitting and optimism in prediction models.
Abstract
Introduction.- Applications of prediction models.- Study design for prediction models.- Statistical models for prediction.- Overfitting and optimism in prediction models.- Choosing between alternative statistical models.- Dealing with missing values.- Case study on dealing with missing values.- Coding of categorical and continuous predictors.- Restrictions on candidate predictors.- Selection of main effects.- Assumptions in regression models: Additivity and linearity.- Modern estimation methods.- Estimation with external methods.- Evaluation of performance.- Clinical usefulness.- Validation of prediction models.- Presentation formats.- Patterns of external validity.- Updating for a new setting.- Updating for a multiple settings.- Prediction of a binary outcome: 30-day mortality after acute myocardial infarction.- Case study on survival analysis: Prediction of secondary cardiovascular events.- Lessons from case studies.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Personalizing Survival Predictions in Advanced Colorectal Cancer: The ARCAD Nomogram Project.

TL;DR: Estimating prognosis on the basis of clinicopathologic factors can inform clinical practice and improve risk stratification for clinical trials and prognostic nomograms have the potential to aid prognostication and patient-physician communication and balance risk in colorectal cancer trials.
Journal ArticleDOI

A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data

TL;DR: The amount of clustering was not meaningfully associated with the models' predictive performance, and backward variable selection had little influence on the model's performance in the authors' simulations.
Journal ArticleDOI

A systematic review and quality assessment of individualised breast cancer risk prediction models

TL;DR: Individualised risk prediction models are promising tools for implementing risk-based screening policies, however, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity.
Related Papers (5)