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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

338
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
338
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

272
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
272
Survival Tree01:19

Survival Tree

335
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...
335
Randomized Experiments01:13

Randomized Experiments

8.7K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.7K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.1K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.1K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

302
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
302

You might also read

Related Articles

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

Sort by
Same author

Challenges in conducting research in community settings and community engagement strategies in response.

BMC global and public health·2026
Same author

Defining the Black population in Canadian health research: a scoping review.

BMC public health·2026
Same author

Collaborative Research Priority Setting for Enhancing Primary Health Care Access Among the Nepalese Community in Canada: Community-Based Participatory Research.

International journal of environmental research and public health·2026
Same author

Oversight and review-based safeguarding initiatives require clear accountability, statutory authority and adequate resources to achieve meaningful change.

Evidence-based nursing·2026
Same author

Navigating Pandemic Hardships: Experiences of Food Insecurity in Racially/Ethnically Diverse Adults in Canada.

Sociology of health & illness·2026
Same author

Breaking barriers: A study protocol on unveiling gender, racial and other intersectional dynamics in post-secondary institutions and identifying solutions for advancing primary care and public health research.

PloS one·2026

Related Experiment Video

Updated: Dec 26, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Variable selection strategies and its importance in clinical prediction modelling.

Mohammad Ziaul Islam Chowdhury1, Tanvir C Turin1,2

  • 1Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

Family Medicine and Community Health
|March 10, 2020
PubMed
Summary

Selecting the right variables is crucial for building accurate clinical prediction models. This guide explains variable selection strategies and techniques for identifying at-risk patients and initiating preventive care.

Keywords:
epidemiology

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

2.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.8K

Related Experiment Videos

Last Updated: Dec 26, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
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

2.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.8K

Area of Science:

  • Medical Informatics
  • Biostatistics
  • Clinical Epidemiology

Background:

  • Clinical prediction models identify at-risk patients for preventive measures.
  • Variable selection is a critical step in developing robust prediction models.
  • Inappropriate variable selection can lead to inaccurate risk identification.

Purpose of the Study:

  • To introduce the concept and importance of variable selection in prediction modeling.
  • To discuss various variable selection strategies and techniques.
  • To highlight the necessity of proper methods for selecting variables.

Main Methods:

  • Discussion of variable selection techniques including backward elimination, forward selection, stepwise selection, and all possible subset selection.
  • Explanation of stopping rules and selection criteria such as p-values, Akaike information criterion (AIC), Bayesian information criterion (BIC), and Mallows' Cp statistic.
  • Focus on the importance of appropriate variable inclusion and methodological rigor.

Main Results:

  • Variable selection is essential for the validity and reliability of clinical prediction models.
  • Different techniques offer various approaches to optimize model performance.
  • Adherence to proper selection criteria ensures model generalizability.

Conclusions:

  • Effective variable selection is paramount for accurate patient risk stratification.
  • Understanding and applying appropriate selection methods enhances clinical decision-making.
  • This paper provides a foundational overview for researchers and clinicians involved in prediction modeling.