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Related Concept Videos

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...
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...
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...
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...
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
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: Jun 4, 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

Likelihood inference on the relative risk in split-cluster designs.

Mohamed M Shoukri1, Dilek Colak, Allan Donner

  • 1Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada. mshoukri@hotmail.com

Clinical Trials (London, England)
|February 22, 2011
PubMed
Summary

Split-cluster designs (SCDs) offer efficient health science research by reducing variation. This study introduces a bivariate beta-binomial model for robust statistical inference in binary outcome analyses, enhancing efficiency over nonparametric methods.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Last Updated: Jun 4, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

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Published on: September 16, 2022

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:

  • Health Sciences Research
  • Biostatistics
  • Clinical Trial Design

Background:

  • Split-cluster designs (SCDs) are utilized in health sciences for nested data structures, offering potential efficiency gains over parallel arm designs.
  • Binary outcomes in SCDs have traditionally relied on less efficient nonparametric statistical methods.
  • Existing methods for analyzing binary outcomes in SCDs may lack the statistical power and efficiency of likelihood-based approaches.

Purpose of the Study:

  • To develop and present a novel bivariate beta-binomial model for statistical inference in split-cluster designs with binary outcomes.
  • To construct score tests and confidence intervals (Wald's and Fieller's) for relative risk (RR) within the SCD framework.
  • To assess the efficiency of the proposed likelihood-based approach compared to existing nonparametric methods.

Main Methods:

  • A bivariate-correlated model was constructed to test the null hypothesis of no treatment effect (RR=1.0) using a score test.
  • Wald- and Fieller-based confidence intervals were developed for the relative risk (RR).
  • A goodness-of-fit procedure was introduced to test the interclass correlation coefficient (H(0):ρ₁₂ = 0) to guide design choices.

Main Results:

  • The methodology was illustrated using two real-world datasets: a split-mouth trial on gingivitis treatment and a mental health study on depression and anxiety.
  • The proposed bivariate beta-binomial model allows for full likelihood statistical inference, offering optimal statistical properties.
  • Efficiency gains were discussed for the proposed approach in split-cluster design settings.

Conclusions:

  • A robust bivariate beta-binomial model was developed for comprehensive statistical inference in split-cluster designs.
  • The likelihood approach, including score tests and Wald's confidence intervals, provides statistically optimal properties for analyzing binary outcomes.
  • The developed methods offer a more efficient alternative to traditional nonparametric approaches for split-cluster designs.