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

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
Critical Values01:31

Critical Values

9.5K
A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
9.5K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

10.0K
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...
10.0K
Confidence Intervals01:21

Confidence Intervals

9.4K
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 confidence...
9.4K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

8.9K
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...
8.9K
Confidence Coefficient01:24

Confidence Coefficient

9.2K
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...
9.2K

You might also read

Related Articles

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

Sort by
Same author

Identification of a novel fiber shaft structural motif and overexpression of key transcripts elucidated in human adenovirus D 10.

PLoS pathogens·2026
Same author

Designing Pleasure-Centered, Culturally Relevant PrEP Messaging for Black Gay, Bisexual, Queer, Same-Gender-Loving, and Other Men Who have Sex with Men (SGL/MSM) in New York City.

AIDS and behavior·2026
Same author

Agile legged locomotion in reconfigurable modular robots.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Tensions in empowerment- or community-based HIV prevention interventions: lessons learned from ETOILE, a collaborative France-US self-study project.

Global health promotion·2026
Same author

Hierarchical Riblet Structures for Enhanced Drag Reduction and Broader Operational Range in Water Pipelines.

ACS ES&T water·2025
Same author

Implementing patient and public involvement (PPI) in eye research: reflections from developing a research study on Geographic Atrophy treatment acceptability.

Research involvement and engagement·2025
Same journal

Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis.

The international journal of biostatistics·2026
Same journal

Predicting birth weight by multivariate functional principal component regressions.

The international journal of biostatistics·2026
Same journal

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same journal

Handling the uncertainty issue of missingness via a mixture-structure-based method.

The international journal of biostatistics·2026
Same journal

Statistical method for pooling categorical biomarker data from multi-center matched/nested case-control studies.

The international journal of biostatistics·2026
Same journal

Prognostic score methods for the estimation of the average causal effect.

The international journal of biostatistics·2026
See all related articles

Related Experiment Video

Updated: May 6, 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

2.3K

Exact nonparametric confidence bands for the survivor function.

David Matthews

    The International Journal of Biostatistics
    |October 16, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for creating precise confidence bands for survival data, crucial for accurate statistical analysis in medical research. The approach ensures reliable interval estimates for the survivor function, even with censored data.

    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

    9.9K
    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
    06:46

    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

    Published on: September 27, 2024

    1.0K

    Related Experiment Videos

    Last Updated: May 6, 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

    2.3K
    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

    9.9K
    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
    06:46

    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

    Published on: September 27, 2024

    1.0K

    Area of Science:

    • Biostatistics
    • Survival Analysis
    • Nonparametric Statistics

    Background:

    • Accurate confidence bands are essential for estimating the survivor function.
    • Existing methods for simultaneous confidence bands have limitations, especially with censored data.

    Purpose of the Study:

    • To derive exact nonparametric confidence bands for the survivor function of any positive random variable.
    • To investigate the effects of right censoring on these confidence bands.

    Main Methods:

    • Inverting a nonparametric likelihood test of uniformity based on the Kaplan-Meier estimator.
    • Utilizing Noe recursions and the Van Wijngaarden-Decker-Brent root-finding algorithm for computation.
    • Applying the method to observational studies of non-Hodgkin's lymphoma and lung cancer patients.

    Main Results:

    • The proposed method yields exact simultaneous lower and upper confidence bands with a specified global confidence level.
    • Monte Carlo simulations confirm the method's effectiveness in providing accurate interval estimates across the observed sample range.
    • The study demonstrates the application and validity of the method in real-world survival data scenarios.

    Conclusions:

    • The derived method provides a robust tool for generating exact simultaneous confidence bands for the survivor function.
    • The approach effectively handles right-censored data, enhancing the reliability of survival analysis.
    • This work offers significant advancements in nonparametric survival analysis and statistical estimation.