Related Experiment Video
Updated: Dec 17, 2025
06:19
Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
1.9K
A general presentation on how to carry out a CHARMS analysis for prognostic multivariate models
Antonio Palazón-Bru1, Francisco Martín-Pérez1, Emma Mares-García1
1Department of Clinical Medicine, Miguel Hernández University, Alicante, Spain.
Statistics in Medicine
|June 26, 2020
Summary
This tutorial explains the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist for evaluating prognostic models. It provides a step-by-step guide and applies it to atrial fibrillation prediction models.
Area of Science:
- Medical Informatics
- Biostatistics
- Epidemiology
Background:
- Systematic reviews require rigorous methodological appraisal of prediction models.
- Existing tools may not fully address the nuances of prognostic multivariate models.
- The CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist was developed to address this need.
Purpose of the Study:
- To provide a general presentation on conducting a CHARMS analysis for prognostic multivariate models.
- To clarify the steps involved in applying the CHARMS checklist to studies within a systematic review.
- To serve as a tutorial resource for researchers and clinicians.
Main Methods:
- Detailed explanation of the CHARMS checklist methodology.
- Step-by-step guidance on applying CHARMS criteria to individual studies.
- Application of the CHARMS method to a real-world case: atrial fibrillation predictive models.
Main Results:
- Demonstration of how to systematically appraise prediction models using CHARMS.
- Illustrates the process with a practical example of atrial fibrillation prediction.
- Provides a framework for assessing the quality and applicability of prognostic models.
Conclusions:
- The CHARMS checklist offers a structured approach for evaluating prognostic models in systematic reviews.
- This methodology facilitates comprehensive information gathering for each included model.
- Enables informed decisions regarding the clinical implementation of predictive models.
Related Concept Videos
01:21Cancer Survival Analysis
573
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
573
01:18Introduction To Survival Analysis
630
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
630
01:20Comparing the Survival Analysis of Two or More Groups
477
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
477
01:15Assumptions of Survival Analysis
309
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
309
01:11Regression Analysis
7.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.6K
01:14Parametric Survival Analysis: Weibull and Exponential Methods
905
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
905