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Related Concept Videos

Hazard Ratio01:12

Hazard Ratio

130
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...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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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...
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Related Experiment Video

Updated: Jul 8, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Fusing trial data for treatment comparisons: Single vs multi-span bridging.

Bonnie E Shook-Sa1, Paul N Zivich2,3, Samuel P Rosin1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Statistics in Medicine
|December 18, 2023
PubMed
Summary

New statistical methods allow comparing therapies across different clinical trials, even without a direct comparison arm. These bridging estimators, particularly the single-span method, offer efficient and unbiased efficacy estimates for treatments like antiretroviral therapy.

Keywords:
causal inferencegeneralizabilitytransportability

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Epidemiology

Background:

  • Randomized controlled trials (RCTs) are essential for therapy efficacy but have limitations in direct treatment comparisons.
  • Comparing treatments evaluated in separate trials is challenging due to potential population differences affecting outcomes.

Purpose of the Study:

  • To introduce and evaluate two novel bridging estimators for comparing treatments across different trials.
  • To account for measured differences in trial populations when making indirect comparisons.
  • To assess the performance of these estimators in simulations and a real-world application.

Main Methods:

  • Two bridging estimators were developed: a "multi-span" estimator using a shared arm and a "single-span" estimator without a shared arm.
  • A diagnostic statistic was created to compare outcomes in standardized shared arms.
  • Simulations were conducted to evaluate estimator bias and confidence interval coverage.
  • The estimators were applied to AIDS Clinical Trials Group (ACTG) 320 and 388 data.

Main Results:

  • Both multi-span and single-span estimators demonstrated minimal empirical bias and nominal confidence interval coverage under identification assumptions.
  • The single-span estimator requires weaker identification assumptions.
  • The single-span approach was more efficient in both simulations and the application to HIV data.

Conclusions:

  • Bridging estimators provide a viable method for indirect treatment comparisons when direct comparisons are not feasible.
  • The single-span estimator is a robust and efficient tool for estimating treatment efficacy across independent trials.
  • This methodology can inform treatment decisions, such as comparing two-drug versus four-drug antiretroviral therapy for advanced HIV.