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

Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

779
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
779
Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.5K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K
Regression Analysis01:11

Regression Analysis

7.2K
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:
7.2K
Survival Tree01:19

Survival Tree

499
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
499

You might also read

Related Articles

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

Sort by
Same author

Missing infrastructure for real-world predictive AI impact.

BMJ health & care informatics·2026
Same author

Using routinely collected data for research purposes: challenges and mitigation strategies.

BMJ (Clinical research ed.)·2026
Same author

Critical appraisal of fairness metrics for artificial intelligence-based clinical prediction models: a scoping review.

The Lancet. Digital health·2026
Same author

Comparing methods for handling missing data in electronic health records for dynamic risk prediction of central-line associated bloodstream infection.

BMC medical research methodology·2026
Same author

Clustered flexible calibration plots for binary outcomes using random effects modeling.

Research synthesis methods·2026
Same author

Diagnostic tests for ovarian cancer in premenopausal women with non-specific symptoms (ROCkeTS): prospective, multicentre, cohort study.

BMJ (Clinical research ed.)·2026

Related Experiment Video

Updated: May 2, 2026

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

2.3K

Risk prediction with machine learning and regression methods.

Ewout W Steyerberg1, Tjeerd van der Ploeg, Ben Van Calster

  • 1Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|March 12, 2014
PubMed
Summary
This summary is machine-generated.

This study explores machine learning for risk prediction, discussing theoretical and applied aspects of probability estimation for various outcomes. It highlights key considerations for developing accurate predictive models in healthcare.

Keywords:
Machine learningPredictionRegression

Related Experiment Videos

Last Updated: May 2, 2026

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

2.3K

Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Risk prediction is crucial in healthcare for patient management and treatment decisions.
  • Machine learning offers advanced methods for probability estimation in medical contexts.
  • Existing literature provides theoretical foundations and practical applications of these methods.

Purpose of the Study:

  • To discuss theoretical challenges in machine learning-based probability estimation for dichotomous and multicategory outcomes.
  • To review practical applications and considerations for implementing these machine learning methods in risk prediction.
  • To synthesize findings from two related papers on the topic.

Main Methods:

  • Review and discussion of theoretical concepts in probability estimation using machine learning.
  • Analysis of applied machine learning techniques for risk prediction in medical research.
  • Synthesis of findings from cited theoretical and application-focused papers.

Main Results:

  • Machine learning methods offer powerful tools for probability estimation in risk prediction.
  • Theoretical considerations include model selection, performance evaluation, and handling different outcome types.
  • Applied aspects involve data preprocessing, algorithm implementation, and interpretation of results.

Conclusions:

  • Accurate risk prediction using machine learning requires careful consideration of both theoretical underpinnings and practical application challenges.
  • Further research and validation are essential for robust implementation of these methods in clinical practice.
  • The discussed papers provide a comprehensive overview for researchers and practitioners in the field.