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Related Concept Videos

Odds Ratio01:09

Odds Ratio

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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Relative Risk01:12

Relative Risk

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...
Hazard Ratio01:12

Hazard Ratio

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 evaluating a...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...

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Related Experiment Video

Updated: May 19, 2026

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

Proportional Odds or Win Probability as Methods for Assessing Ordinal Outcomes in Infectious Disease Clinical Trials.

Linda J Harrison1, Felicia C Chow2,3

  • 1Center for Biostatistics in AIDS Research, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

The Journal of Infectious Diseases
|May 18, 2026
PubMed
Summary

This study reviews two key statistics, the proportional odds ratio (pOR) and win probability, for analyzing ordinal outcomes in infectious disease trials. Visual tools like stacked-bar charts enhance the interpretation of these trial results.

Keywords:
clinical trialsinfectious diseaseordinal outcomesproportional odds ratiowin probability

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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

Related Experiment Videos

Last Updated: May 19, 2026

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

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

Area of Science:

  • Clinical Trials
  • Biostatistics
  • Infectious Diseases

Background:

  • Ordinal outcomes are frequently used in infectious disease trials to assess intervention effectiveness.
  • Existing statistical methods may not fully capture the nuances of ranked categorical data.

Purpose of the Study:

  • To review and compare two summary statistics for ordinal outcomes: proportional odds ratio (pOR) and win probability.
  • To introduce visual aids for improved accessibility and interpretability of ordinal trial data.

Main Methods:

  • Review of proportional odds ratio (pOR) for comparing improved categories between treatment and control groups.
  • Explanation of win probability as the chance of a treated participant faring better than a control participant.
  • Introduction of stacked-bar charts and bubble plots as visual companions for ordinal data.

Main Results:

  • The proportional odds ratio (pOR) quantifies the likelihood of improvement in treated versus control participants.
  • The win probability provides a direct measure of treatment benefit in pairwise comparisons.
  • Visualizations like stacked-bar charts and bubble plots aid in understanding complex ordinal data.

Conclusions:

  • Proportional odds ratio (pOR) and win probability are valuable statistics for analyzing ordinal outcomes in clinical research.
  • Visual tools enhance the communication and understanding of infectious disease trial results.
  • These methods improve the comprehensive assessment of intervention effects using ranked categorical data.