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

Hazard Rate01:11

Hazard Rate

509
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...
509
Hazard Ratio01:12

Hazard Ratio

701
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...
701
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

487
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
487
Censoring Survival Data01:09

Censoring Survival Data

647
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
647
Sample Size Calculation01:19

Sample Size Calculation

6.9K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.9K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K

You might also read

Related Articles

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

Sort by
Same author

How should covariates be handled in randomized trials? Empirical evidence from 50 trials and recommendations for practice.

Journal of clinical epidemiology·2026
Same author

Estimating Baseline Survival Function in the Proportional Hazards Model Under Monotone Hazards.

Journal of statistical theory and practice·2026
Same author

A quantitative framework to assess the potential of earlier cancer detection to improve cancer survival.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Histotripsy dose impacts tumor cellular damage and treatment outcomes in a preclinical model of hepatocellular carcinoma.

Scientific reports·2026
Same author

Gemcitabine plus nivolumab with carboplatin or oxaliplatin in cisplatin-ineligible patients with metastatic urothelial carcinoma: a randomized phase II trial.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

OPERA: a new algorithm for patient stratification based on partially ordered risk factors.

Biometrics·2026
Same journal

A statistical evaluation of decision-making methods and the efficiency of Bayesian multi-arm multi-stage trials.

Clinical trials (London, England)·2026
Same journal

Accounting for non-adherence: A re-analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial.

Clinical trials (London, England)·2026
Same journal

Phase I design for partially ordered groups with late-onset toxicity.

Clinical trials (London, England)·2026
Same journal

Trial informed consent forms, the Declaration of Helsinki and the SPIRIT 2025 statement.

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Morning panel discussion).

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
See all related articles

Related Experiment Video

Updated: Mar 30, 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

15.5K

Sample size under the additive hazards model.

Lee S McDaniel1, Menggang Yu2, Rick Chappell3

  • 1Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA lmcda4@lsuhsc.edu.

Clinical Trials (London, England)
|November 18, 2015
PubMed
Summary
This summary is machine-generated.

New sample size formulas for additive hazards models in clinical trials offer improved interpretability over proportional hazards models. These formulas, validated by simulations, aid in designing superiority and non-inferiority trials for time-to-event outcomes.

Keywords:
Sample sizeadditive hazardsnon-inferioritytime-to-event

More Related Videos

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

11.0K
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

2.7K

Related Experiment Videos

Last Updated: Mar 30, 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

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

11.0K
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

2.7K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Additive hazards models offer advantages in interpretability and fit compared to proportional hazards models.
  • Current sample size calculations for time-to-event outcomes often rely on proportional or constant hazards assumptions.

Purpose of the Study:

  • To develop and evaluate sample size formulas for superiority and non-inferiority trials based on the additive hazards model.
  • To provide formulas that do not require specific distributional assumptions for time-to-event data.

Main Methods:

  • Derivation of sample size formulas for additive hazards models.
  • Monte Carlo simulations to verify the accuracy and power of the derived formulas.
  • Application of the non-inferiority sample size formula to a real-world clinical trial (SPORTIF III).

Main Results:

  • Simulation results confirm that the developed sample size formulas achieve the desired statistical power.
  • The formulas provide accurate sample size calculations under the additive hazards assumption.
  • The methodology was successfully applied to a stroke prevention trial.

Conclusions:

  • The proposed sample size formulas for additive hazards models are effective and validated through simulations.
  • A user-friendly web tool is available for calculating sample sizes, simplifying trial design.
  • These advancements facilitate more accurate and interpretable clinical trial planning.