Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Data Validation01:15

Data Validation

1.8K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
1.8K
Data Validation01:03

Data Validation

6.8K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.8K
Censoring Survival Data01:09

Censoring Survival Data

550
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
550
Regression Toward the Mean01:52

Regression Toward the Mean

7.0K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.0K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

276
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
276
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

559
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
559

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: Circulating nitric oxide pathway metabolites in heart failure with preserved ejection fraction: a sex-stratified cross-sectional analysis.

Biology of sex differences·2026
Same author

Exercise-induced changes in hemostasis markers in marathon runners: effects of enzyme supplementation and determinants.

Frontiers in physiology·2026
Same author

Exercise in regional breast cancer with neoadjuvant anthracycline-based chemotherapy with immune checkpoint-inhibition: study protocol for a prospective randomised controlled trial.

Frontiers in oncology·2026
Same author

Circulating nitric oxide pathway metabolites in heart failure with preserved ejection fraction: a sex-stratified cross-sectional analysis.

Biology of sex differences·2026
Same author

Impact of multicomponent exercise training in older individuals with and without heart failure with reduced ejection fraction - the role of functional capacity and novel biomarkers.

European journal of heart failure·2026
Same author

Assessment of nephrotoxicity following lutetium-177 PSMA I&T radioligand therapy: a comparative study with docetaxel chemotherapy.

European journal of nuclear medicine and molecular imaging·2026

Related Experiment Video

Updated: Feb 3, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

Overview of model validation for survival regression model with competing risks using melanoma study data.

Zhongheng Zhang1, Giuliana Cortese2, Christophe Combescure3

  • 1Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.

Annals of Translational Medicine
|October 27, 2018
PubMed
Summary

This article details validating competing risks regression models using R packages. It covers assessing prediction accuracy with discrimination and calibration metrics, and recommends cross-validation over data splitting for robust results.

Keywords:
Calibration plotcompeting riskdiscriminationprediction model

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.6K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

912

Related Experiment Videos

Last Updated: Feb 3, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.6K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

912

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Machine Learning in Healthcare

Background:

  • Competing risks regression models are crucial for predicting events when multiple outcomes are possible.
  • Assessing prediction accuracy requires evaluating both discrimination and calibration of these models.
  • Traditional methods like one-time data splitting have limitations in sample size reduction and result variability.

Discussion:

  • This review focuses on validating competing risks regression models, emphasizing discrimination and calibration metrics.
  • It highlights the advantages of cross-validation over simple data splitting for more reliable model assessment.
  • The article introduces practical implementation using the R package 'riskRegression' for time-to-event data analysis.

Key Insights:

  • Cross-validation is a preferred method for validating competing risks models due to its robustness.
  • Metrics such as Area Under the Curve (AUC) and Concordance-index assess discrimination, while calibration plots evaluate accuracy.
  • The 'riskRegression' package in R facilitates model validation and nomogram plotting for competing risks.

Outlook:

  • Future research should explore advanced validation techniques for complex competing risks scenarios.
  • Integration of these validation methods into clinical practice can improve the reliability of risk predictions.
  • Development of user-friendly tools for model validation will enhance their adoption in biomedical research.