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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

450
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
450
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

880
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...
880
Hazard Rate01:11

Hazard Rate

314
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...
314
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

351
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
351
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

295
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.
295
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.2K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Convergent Roles of Growth Differentiation Factor-15 (GDF-15) in Mechanotransduction, Vascular Disorganization, and Immune Suppression in Melanoma.

bioRxiv : the preprint server for biology·2026
Same author

COVID-19 Pandemic-Related Perceived Loneliness as a Potential Risk Factor for Worse Outcomes Among People Who are Pregnant or Postpartum.

WMJ : official publication of the State Medical Society of Wisconsin·2025
Same author

Tumor Heterogeneity Shapes Survival Dynamics in Drug-Treated Cells, Revealing Size-Drifting Subpopulations.

ACS pharmacology & translational science·2024
Same author

Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan.

Nature communications·2024
Same author

The most effective corticosteroid dose in the treatment of glenohumeral osteoarthritis: Feasibility pilot and protocol for double blinded randomized controlled trial.

Osteoarthritis and cartilage open·2024
Same author

Some teaching resources using R with illustrative examples exploring COVID-19 data.

Teaching statistics·2024
Same journal

Ordinal pattern-based change point detection.

Test (Madrid, Spain)·2025
Same journal

A generalized Hosmer-Lemeshow goodness-of-fit test for a family of generalized linear models.

Test (Madrid, Spain)·2024
Same journal

Power priors for replication studies.

Test (Madrid, Spain)·2024
Same journal

Level sets of depth measures in abstract spaces.

Test (Madrid, Spain)·2023
Same journal

Robust and efficient estimation of nonparametric generalized linear models.

Test (Madrid, Spain)·2023
Same journal

Homogeneity tests for one-way models with dependent errors under correlated groups.

Test (Madrid, Spain)·2022
See all related articles

Related Experiment Video

Updated: Dec 9, 2025

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

10.6K

Reduced bias nonparametric lifetime density and hazard estimation.

Arthur Berg1, Dimitris Politis2, Kagba Suaray3

  • 1Penn State College of Medicine, Division of Biostatistics & Bioinformatics.

Test (Madrid, Spain)
|September 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel, adaptive kernel-based method for estimating hazard rates from censored survival data. The new approach offers improved accuracy and efficiency, outperforming existing nonparametric methods.

Keywords:
Bandwidth estimationDensity estimationFourier transformHazard function estimationInfinite-order kernelsNonparametric estimationSurvival analysis

More Related Videos

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

35.2K
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

14.9K

Related Experiment Videos

Last Updated: Dec 9, 2025

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

10.6K
Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

35.2K
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

14.9K

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Accurate hazard rate estimation is crucial for survival data analysis.
  • Existing nonparametric methods often face challenges with bias and mean squared error.
  • Censored data is common in medical and reliability studies, complicating estimation.

Purpose of the Study:

  • To develop a fully automatic and adaptive kernel-based hazard rate estimator.
  • To improve bias and mean squared error properties using infinite-order kernels.
  • To enhance efficiency and convergence rates for nonparametric survival data analysis.

Main Methods:

  • Utilized a special class of infinite-order kernels for hazard rate estimation.
  • Developed a fully automatic and adaptive implementation for randomly right-censored data.
  • Employed careful bandwidth selection for optimal mean squared error performance.

Main Results:

  • The proposed estimator demonstrated improved accuracy compared to existing nonparametric methods.
  • Achieved favorable bias and mean square error properties.
  • Showcased nearly parametric convergence rates in certain scenarios.

Conclusions:

  • The novel kernel-based method provides a more efficient and accurate approach to hazard rate estimation.
  • The adaptive implementation is suitable for practical applications with censored survival data.
  • The method was validated on a large dataset of breast carcinoma patients.