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

Odds Ratio01:09

Odds Ratio

2.4K
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...
2.4K
Hazard Ratio01:12

Hazard Ratio

737
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...
737
Relative Risk01:12

Relative Risk

2.6K
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...
2.6K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

579
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,...
579
Randomized Experiments01:13

Randomized Experiments

9.4K
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...
9.4K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

725
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...
725

You might also read

Related Articles

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

Sort by
Same author

Effect of Probiotic Supplementation on Gut Microbiota in Children with Autism: A Pilot Randomised Controlled Trial.

Nutrients·2026
Same author

Effect of Extended Infusion versus Intermittent Bolus Feedings on Hospital Weight Gain in Extremely Low Birth Weight Infants Born at <29 Weeks of Gestation: A Randomized Non-Inferiority Clinical Trial.

The Journal of pediatrics·2026
Same author

A randomized controlled trial to evaluate the efficacy and safety of a double-walled incubator compared to a radiant warmer in the care of extremely low-birth-weight infants.

Journal of tropical pediatrics·2026
Same author

Topical glyceryl trinitrate for increasing radial arterial diameter in neonates: a randomised controlled trial.

Pediatric research·2026
Same author

Delayed versus Early Umbilical Cord Clamping in Preterm Multiple Gestation: A Randomized Controlled Trial.

American journal of perinatology·2026
Same author

Psychological safety in healthcare - helping everyone to speak up.

Seminars in fetal & neonatal medicine·2026

Related Experiment Video

Updated: Apr 16, 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

2.8K

Odds ratio vs risk ratio in randomized controlled trials.

Haribalakrishna Balasubramanian1, Anitha Ananthan, Shripada Rao

  • 1Department of Neonatology, King Edward Memorial Hospital for women and newborns , Perth, Western Australia , Australia.

Postgraduate Medicine
|March 10, 2015
PubMed
Summary

Odds ratios (OR) in randomized controlled trials (RCTs) can overestimate effect size compared to risk ratios (RR). While not altering statistical significance, ORs can mislead clinicians due to exaggerated effect sizes, especially with high outcome prevalence.

Keywords:
Odds ratioeffect sizerandomized controlled trialsrisk ratio

More Related Videos

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

15.5K
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

11.1K

Related Experiment Videos

Last Updated: Apr 16, 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

2.8K
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

15.5K
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

11.1K

Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Evidence-Based Medicine

Background:

  • Odds ratios (OR) are frequently used in randomized controlled trials (RCTs).
  • Criticism exists that ORs overestimate effect size when misinterpreted as risk ratios (RR).
  • The clinical impact of this overestimation in RCTs remains unclear.

Purpose of the Study:

  • To quantify the extent to which ORs overestimate effect sizes compared to RRs in RCTs.
  • To assess the implications for clinical research and evidence-based practice.

Main Methods:

  • A review of 107 RCTs published in the New England Journal of Medicine (2004-2014) reporting primary outcomes as RR or OR.
  • Calculation of RRs from reported ORs and vice versa using Stata software.
  • Analysis of the percentage of divergence between reported and calculated effect size estimates.

Main Results:

  • Odds ratios exaggerated the risk ratio in 62% of the analyzed RCTs.
  • Overestimation exceeded 50% in 28 RCTs and 100% in 13 RCTs.
  • The degree of overestimation correlated positively with outcome prevalence (Spearman's rho = 0.84 and 0.66, p < 0.001).

Conclusions:

  • Using OR instead of RR in RCTs does not alter the qualitative interpretation of statistical significance.
  • However, ORs can significantly exaggerate effect sizes, potentially misleading clinicians.
  • Awareness of this exaggeration is crucial for accurate interpretation in evidence-based medicine.