Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Survival Curves

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

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.

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

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...

Exponential Equations for Modeling Growth

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...

You might also read

Related Articles

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

Sort by

Same author

Bi-<i>s</i>*-Concave Distributions.

Journal of statistical planning and inference·2021

Same author

The Bennett-Orlicz Norm.

Sankhya. Series A. (2008)·2018

Same author

Entropy of Convex Functions on ℝ <i></i>.

Constructive approximation·2018

Same author

Exponential bounds for the hypergeometric distribution.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability·2018

Same author

GLOBAL RATES OF CONVERGENCE OF THE MLES OF LOG-CONCAVE AND <i>s</i>-CONCAVE DENSITIES.

Annals of statistics·2017

Same author

APPROXIMATION AND ESTIMATION OF <i>s</i>-CONCAVE DENSITIES VIA RÉNYI DIVERGENCES.

Annals of statistics·2017

Search research articles

Related Experiment Video

Updated: Jul 11, 2026

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

On an exponential bound for the Kaplan-Meier estimator.

Jon A Wellner¹

¹Department of Statistics, University of Washington, 354322, Seattle, WA 98195-4322, USA. jaw@stat.washington.edu

Lifetime Data Analysis

|September 7, 2007

Summary

This study reviews limit theory and inequalities for the Kaplan-Meier estimator, crucial for survival function analysis. It also provides bounds and numerical evidence for a key inequality in survival analysis.

Area of Science:

Statistics
Survival Analysis

Background:

The Kaplan-Meier estimator is a fundamental tool for analyzing survival data.
Understanding its theoretical properties, including limit theory and inequalities, is essential for accurate survival function estimation.

Purpose of the Study:

To review and synthesize existing limit theory and inequalities for the Kaplan-Meier product limit estimator.
To provide bounds for a constant in a significant inequality by Biotouzé et al.
To offer numerical support for a conjecture within survival analysis.

Main Methods:

Review of established statistical limit theory.
Analysis of inequalities related to the Kaplan-Meier estimator.
Derivation of bounds for specific constants.
Numerical simulations to test conjectures.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: Jul 11, 2026

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Main Results:

Consolidated review of limit theory and inequalities for the Kaplan-Meier estimator.
Established bounds for a constant in the Biotouzé et al. inequality.
Numerical evidence supporting a conjecture in survival analysis.

Conclusions:

The study enhances the theoretical understanding of the Kaplan-Meier estimator.
The findings contribute to the validation of existing inequalities and conjectures in survival analysis.
Provides a foundation for further research in survival data analysis.