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

Relative Risk01:12

Relative Risk

117
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
117
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

121
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,...
121
Odds Ratio01:09

Odds Ratio

105
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
105
Hazard Ratio01:12

Hazard Ratio

91
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
91
Actuarial Approach01:20

Actuarial Approach

63
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
63
Ratio Level of Measurement00:54

Ratio Level of Measurement

17.2K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
17.2K

You might also read

Related Articles

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

Sort by
Same author

Reducing Psychotropic Medication Use in Foster-Care Children with a Personalized Medication Review.

Journal of child and adolescent psychopharmacology·2025
Same author

Comparing and intervening on behavioral demand for snack foods among justice-involved adolescents: A preliminary translational analysis.

Journal of the experimental analysis of behavior·2024
Same author

An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation.

Perspectives on behavior science·2020
Same author

Discriminative and participant-rated effects of methylphenidate in children diagnosed with attention deficit hyperactivity disorder (ADHD).

Experimental and clinical psychopharmacology·1998
Same journal

A Mediational Theory of Verbal Relations.

Perspectives on behavior science·2026
Same journal

It is Time to Retire "Noncontingent Reinforcement".

Perspectives on behavior science·2026
Same journal

Using Wearable Technology to Predict the Occurrence of Severe Behavior Problems among Neurodiverse Individuals: A Systematic Review.

Perspectives on behavior science·2026
Same journal

Toward a Modern View of Pavlovian Conditioning in Applied Behavior Analysis.

Perspectives on behavior science·2026
Same journal

Behavior, Process, and Evolution in the Multiscale Molar Paradigm.

Perspectives on behavior science·2026
Same journal

Citing the Literature.

Perspectives on behavior science·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

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

2.0K

The Proper Calculation of Risk Ratios: How and Why.

M Christopher Newland1

  • 1Department of Psychological Science, Auburn University, Auburn, AL USA.

Perspectives on Behavior Science
|November 25, 2024
PubMed
Summary
This summary is machine-generated.

This study corrects the calculation and application of risk ratios (relative risk) in behavior analysis. It provides guidance on statistical significance, data stability, and visual presentation for accurate scientific reporting.

Keywords:
Behavior analysisForest plot; ProbabilityRelative riskRisk ratio

More Related Videos

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

10.1K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K

Related Experiment Videos

Last Updated: Jun 6, 2025

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

2.0K
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

10.1K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K

Area of Science:

  • Behavior analysis
  • Quantitative methods in behavioral science

Background:

  • A recent article proposed risk ratios (relative risk) for behavior analysis.
  • The proposed method contained computational errors and lacked nuance.

Purpose of the Study:

  • To correct the calculation of risk ratios in behavior analysis.
  • To provide guidance on the appropriate use and interpretation of risk ratios.
  • To enhance the scientific rigor and application of risk ratios.

Main Methods:

  • Recalculation of risk ratios based on established statistical principles.
  • Statistical tests to determine if a risk ratio differs from a reference group.
  • Analysis of data stability for reliable risk ratio computation.
  • Demonstration of visual presentation methods, including Forest plots.

Main Results:

  • Identified and corrected errors in the previously proposed risk ratio calculation.
  • Provided a framework for assessing the statistical significance of risk ratios.
  • Highlighted the importance of data stability in risk ratio analysis.
  • Illustrated effective methods for visually representing risk ratios.

Conclusions:

  • Accurate calculation and interpretation of risk ratios are crucial for behavior analysis.
  • Corrected methods improve the scientific validity and practical application of risk ratios.
  • Visualizations like Forest plots aid in understanding risk ratio data.