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05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Risk prediction models: II. External validation, model updating, and impact assessment
Karel G M Moons1, Andre Pascal Kengne, Diederick E Grobbee
1Julius Centre for Health Sciences and Primary Care, UMC Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands. k.g.m.moons@umcutrecht.nl
Heart (British Cardiac Society)
|March 9, 2012
Summary
This paper details external validation, model updating, and impact studies for clinical prediction models. It guides improving cardiovascular care through accurate predictions and better decision-making.
Area of Science:
- Cardiovascular Medicine
- Biostatistics
- Clinical Epidemiology
Background:
- Clinical prediction models are vital tools in modern medicine, especially in cardiology, for enhancing clinical reasoning and decision-making.
- Accurate and validated probability estimates are crucial for the effective use of these models.
- Previous work discussed model development, internal validation, and assessing new biomarkers.
Purpose of the Study:
- To outline the process of assessing prediction model performance in new populations (external validation).
- To describe methods for updating existing models with local data or new predictors.
- To explain how to study the impact of prediction models on clinical practice and patient outcomes.
Main Methods:
- External validation studies to assess generalizability.
- Model updating and adjustment techniques.
- Impact studies to evaluate clinical decision-making and patient outcomes.
Main Results:
- Provides a framework for robust external validation of cardiovascular prediction models.
- Demonstrates methods for adapting models to diverse clinical settings.
- Illustrates how to measure the real-world impact of prediction models on healthcare.
Conclusions:
- External validation and model updating are essential for reliable clinical prediction models.
- Assessing the impact of models is key to improving patient care and cost-effectiveness.
- Cardiovascular prediction models, when rigorously validated and implemented, can significantly advance medical practice.
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