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

Confidence Intervals01:21

Confidence Intervals

10.7K
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...
10.7K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.0K
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...
10.0K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

11.7K
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...
11.7K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.9K
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...
8.9K
Confidence Coefficient01:24

Confidence Coefficient

10.6K
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...
10.6K
Cumulative Frequency Distribution01:04

Cumulative Frequency Distribution

8.4K
A cumulative frequency distribution is another type of frequency distribution. Instead of reporting how many data values fall in some classes, it reports how many data values are contained in either that class or any class to its left. Technically, it means the sum of frequencies of the class and all the classes below it in a frequency distribution. A cumulative frequency is calculated by adding the frequency of each class lower than the corresponding class interval or category. In general, a...
8.4K

You might also read

Related Articles

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

Sort by
Same author

Sample Size Recalculation in Adaptive Group Sequential Study Designs for Comparing Restricted Mean Survival Times.

Statistics in medicine·2026
Same author

Medial Unicompartmental Versus Total Knee Arthroplasty in the Treatment of Isolated Anteromedial Knee Osteoarthritis: Two-Year Results from a Double-Blinded, Multicenter, Randomized Trial of 350 Patients.

The Journal of bone and joint surgery. American volume·2026
Same author

Empirical Likelihood Comparison of Absolute Risks.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Correction to "Propensity weighting plus adjustment in proportional hazards model is not doubly robust," by Erin E. Gabriel, Michael C. Sachs, Ingeborg Waernbaum, Els Goetghebeur, Paul F. Blanche, Stijn Vansteelandt, Arvid Sjölander, and Thomas Scheike; Volume 80, Issue 3, September 2024, https://doi.org/10.1093/biomtc/ujae069.

Biometrics·2025
Same author

Propensity weighting plus adjustment in proportional hazards model is not doubly robust.

Biometrics·2024
Same author

Efficacy and safety of oral anticoagulants according to kidney function among patients with atrial fibrillation.

European heart journal. Cardiovascular pharmacotherapy·2024

Related Experiment Video

Updated: Feb 1, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.5K

Confidence intervals for the cumulative incidence function via constrained NPMLE.

Paul Blanche1,2,3

  • 1Section of Biostatistics, University of Copenhagen, Øster Farimagsgade 5B, P.O.B. 2099, 1014, Copenhagen K, Denmark. pabl@sund.ku.dk.

Lifetime Data Analysis
|December 13, 2018
PubMed
Summary

This study introduces two novel methods for calculating non-parametric confidence intervals for the cumulative incidence function (CIF) in competing risks scenarios. These new methods offer more accurate estimates, especially with limited data, improving medical research analysis.

Keywords:
BootstrapCensoringCompeting risksConstrained maximum likelihoodEmpirical likelihoodProfile likelihood

More Related Videos

Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials
06:05

Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials

Published on: January 15, 2014

8.3K
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

2.6K

Related Experiment Videos

Last Updated: Feb 1, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.5K
Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials
06:05

Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials

Published on: January 15, 2014

8.3K
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

2.6K

Area of Science:

  • Biostatistics
  • Medical Statistics
  • Survival Analysis

Background:

  • Competing risks are common in medical research, necessitating accurate analysis of the cumulative incidence function (CIF).
  • Existing methods for confidence intervals of CIF may lack accuracy, particularly in specific data scenarios.

Purpose of the Study:

  • To introduce and evaluate two new non-parametric methods for computing confidence intervals for the CIF in competing risks settings.
  • To provide more accurate confidence intervals than traditional Wald-type intervals, especially for small to moderate sample sizes with few events.

Main Methods:

  • Development of non-parametric profile-likelihood confidence intervals based on constrained non-parametric maximum likelihood estimation (NPMLE).
  • Introduction of constrained bootstrap confidence intervals, also building on constrained NPMLE.
  • Comparison of proposed methods with existing benchmarks via simulation studies.

Main Results:

  • The proposed profile-likelihood and bootstrap methods provide more accurate confidence intervals compared to standard Wald-type intervals.
  • These new methods demonstrate superior performance in scenarios with small to moderate sample sizes and a limited number of observed events.
  • Simulation results validate the effectiveness of the novel approaches.

Conclusions:

  • The new non-parametric profile-likelihood and bootstrap confidence intervals are valuable tools for analyzing CIF in competing risks.
  • These methods offer improved accuracy and reliability, particularly beneficial in medical research with common data limitations.
  • The application to melanoma data demonstrates the practical utility of these advanced statistical techniques.