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

The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

334
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
334
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.7K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
5.7K
Confidence Intervals01:21

Confidence Intervals

6.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.2K
Confidence Coefficient01:24

Confidence Coefficient

7.6K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.6K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
3.1K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.2K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.2K

You might also read

Related Articles

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

Sort by
Same author

Causality-Preserving Domain Generalization via Adaptive Fourier Mixup for RUL Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Likelihood Confidence Intervals for Misspecified Cox Models.

Statistics in medicine·2026
Same author

Information-based group sequential design for post-market safety monitoring of medical products using real world data.

Pharmaceutical statistics·2024
Same author

Associations of dietary inflammatory potential with postpartum weight change and retention: Results from a cohort study.

Obesity (Silver Spring, Md.)·2021
Same author

Down-regulated placental miR-21 contributes to preeclampsia through targeting RASA1.

Hypertension in pregnancy·2021
Same author

Long-term clinical observation of patients with acute and chronic complete spinal cord injury after transplantation of NeuroRegen scaffold.

Science China. Life sciences·2021

Related Experiment Video

Updated: Jun 17, 2025

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

2.0K

Exact test and exact confidence interval for the Cox model.

Yongwu Shao1, Zhishen Ye1, Zhiwei Zhang1

  • 1Biometrics, Gilead Sciences, Foster City, California, USA.

Statistics in Medicine
|August 7, 2024
PubMed
Summary

This study introduces an exact test for the Cox proportional hazards model, offering reliable analysis for clinical trials with few events. This method ensures accurate efficacy and equivalence testing, especially for rare events or highly effective treatments.

Keywords:
Coxexact confidence intervalexact testproportional hazards model

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

10.1K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.0K

Related Experiment Videos

Last Updated: Jun 17, 2025

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

2.0K
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

10.1K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.0K

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • The Cox proportional hazards model is standard for time-to-event data analysis in clinical trials.
  • Asymptotic approximations in standard Cox model inference can be unreliable with few events, common in rare outcomes or highly efficacious treatments.

Purpose of the Study:

  • To propose an exact test for equivalence and efficacy under a proportional hazards model.
  • To develop an exact confidence interval by inverting the proposed exact test.

Main Methods:

  • The proposed exact test utilizes a conditional error method, originally for sample size reestimation.
  • Information is combined from a series of hypergeometric distributions, updated at each observed event time.

Main Results:

  • Simulation studies demonstrate the performance of the proposed exact procedures.
  • The methods are illustrated using real-world data from an HIV prevention trial.

Conclusions:

  • The developed exact test and confidence interval provide a robust alternative for Cox model inference, particularly in scenarios with limited events.
  • The companion R package "ExactCox" facilitates the application of these novel statistical procedures.