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

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,...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

Introduction To Survival Analysis

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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...

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.

You might also read

Related Articles

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

Sort by

Same author

Risk, Predictors, and Outcomes of Acute Kidney Injury in Patients Admitted to Intensive Care Units in Egypt.

Scientific reports·2017

Same author

Long-Term Progression of Coronary Artery Calcification Is Independent of Classical Risk Factors, C-Reactive Protein, and Parathyroid Hormone in Renal Transplant Patients.

Cardiorenal medicine·2017

Same author

Obesity and kidney disease: Hidden consequences of the epidemic.

African journal of primary health care & family medicine·2017

Same author

Validation of echocardiographic criteria for the clinical diagnosis of heart failure in chronic kidney disease.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association·2017

Same author

Optimizing hypertension management in renal transplantation: a call to action.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association·2017

Same author

Optimizing hypertension management in renal transplantation: a call to action.

Journal of hypertension·2017

Search research articles

Related Experiment Video

Updated: Jul 4, 2026

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

The analysis of survival data: the Kaplan-Meier method.

Kitty J Jager¹, Paul C van Dijk, Carmine Zoccali

¹ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. k.j.jager@amc.uva.nl

Kidney International

|July 4, 2008

Summary

This paper explains the Kaplan-Meier method for survival analysis, a key tool for assessing patient prognosis and treatment effectiveness. It details how to calculate survival probabilities and compare outcomes between groups to understand event incidence rates.

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

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

Related Experiment Videos

Last Updated: Jul 4, 2026

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

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

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

Area of Science:

Biostatistics
Medical Research Methodology

Background:

Survival analysis is crucial for determining patient prognosis, evaluating treatment efficacy, and understanding disease progression.
Key questions in clinical research involve predicting patient longevity and the impact of co-morbidities on outcomes like transplantation.

Purpose of the Study:

To provide a comprehensive explanation of the Kaplan-Meier method, the most widely used technique for survival analysis.
To elucidate the calculation of survival probabilities, data summarization, and group comparisons using the logrank test.

Main Methods:

Focuses on the Kaplan-Meier method for survival analysis.
Explains the calculation of event incidence rates (e.g., recovery, myocardial infarction, death).
Details hypothesis testing using the logrank test for comparing survival data between groups.

Main Results:

The Kaplan-Meier method allows for the calculation of survival probabilities over time.
It enables the summarization of survival data and comparison of survival experiences across different patient groups.
Guidance is provided on the effective presentation of survival plots.

Conclusions:

The Kaplan-Meier method is a fundamental tool for survival data analysis in clinical research.
Understanding its application is essential for interpreting patient prognosis and treatment outcomes.
The study also acknowledges the limitations of the Kaplan-Meier method and points to alternative approaches for specific analytical needs.