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Related Concept Videos

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...
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Randomized Experiments01:13

Randomized Experiments

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...
Clinical Trials01:16

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...

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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

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Design and Analysis of Randomized Clinical Trials With Average Hazard: Practical Guidance and Tools for

Miki Horiguchi1, Lu Tian2, Satoshi Hattori3

  • 1Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

Statistics in Medicine
|June 15, 2026
PubMed
Summary

This study introduces average hazard (AH) as an alternative to the traditional hazard ratio (HR) for analyzing time-to-event data in clinical trials. It provides practical guidance and tools for implementing AH in randomized clinical trials.

Keywords:
average intensitydata monitoringperson‐time incidence raterestricted mean survival timesample size

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: Jun 16, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

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

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • The log-rank test and hazard ratio (HR) are standard for time-to-event outcomes in randomized clinical trials (RCTs).
  • Concerns exist regarding the limitations of HR as a sole measure for intervention effect magnitude.
  • Average hazard (AH) offers an alternative interpretation as an average incidence rate over time.

Purpose of the Study:

  • To provide practical guidance for using average hazard (AH) in the primary analysis of RCTs.
  • To address design considerations and timing tools for AH-based analyses.
  • To introduce the `survAHtools` R package for practical AH implementation.

Main Methods:

  • The study proposes a framework for designing RCTs with AH as the primary analysis method.
  • It develops tools to aid in determining the optimal timing for AH-based statistical analyses.
  • A new R package, `survAHtools`, is introduced to facilitate these applications.

Main Results:

  • A structured approach for study design incorporating AH is presented.
  • Tools for selecting appropriate analysis timing for AH are provided.
  • The `survAHtools` package offers practical support for researchers.

Conclusions:

  • Average hazard (AH) presents a viable alternative to hazard ratio (HR) for summarizing intervention effects in time-to-event analyses.
  • The developed framework and tools, including the `survAHtools` package, enhance the practical application of AH in RCTs.
  • This work addresses the need for clearer guidance on implementing AH in clinical trial primary analyses.