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

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.

Censoring Survival Data

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...

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...

Actuarial Approach

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...

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,...

You might also read

Related Articles

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

Sort by

Same author

On-treatment serum prostate-specific antigen and overall survival in prostate cancer (STAMPEDE platform protocol): a post-hoc analysis of data from five phase 3 trials.

The Lancet. Oncology·2026

Same author

Quantification of Abdominal Aortic Calcification on CT: Clinical Validation for Assessment Of Cardiovascular Risk in Oncology.

Journal of imaging informatics in medicine·2026

Same author

Tumor transcriptome-wide expression classifiers predict treatment sensitivity in advanced prostate cancers.

Cell·2025

Same author

Determining the Impact of Histology on the Incidence, Pattern, and Timing of Recurrences in Patients with Renal Cell Carcinoma: A Pooled Analysis from the SORCE and ASSURE Trials.

European urology open science·2025

Same author

Metformin for patients with metastatic prostate cancer starting androgen deprivation therapy: a randomised phase 3 trial of the STAMPEDE platform protocol.

The Lancet. Oncology·2025

Same author

External validation of a digital pathology-based multimodal artificial intelligence-derived prognostic model in patients with advanced prostate cancer starting long-term androgen deprivation therapy: a post-hoc ancillary biomarker study of four phase 3 randomised controlled trials of the STAMPEDE platform protocol.

The Lancet. Digital health·2025

Search research articles

Related Experiment Video

Updated: May 20, 2026

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

A simulation study of predictive ability measures in a survival model II: explained randomness and predictive

B Choodari-Oskooei¹, P Royston, Mahesh K B Parmar

¹London Hub for Trials Methodology Research, MRC Clinical Trials Unit, Aviation House, London, WC2B 6NH, UK. bbo@ctu.mrc.ac.uk

Statistics in Medicine

|July 6, 2012

Summary

This study evaluates R(2)-type measures for survival model prediction. The Kent and O'Quigley (ρ(W)(2)) and Schemper and Kaider (R(SK)(2)) measures performed best, though ρ(W)(2) showed sensitivity to covariates and influential data.

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

Related Experiment Videos

Last Updated: May 20, 2026

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

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

Area of Science:

Statistics
Biostatistics
Survival Analysis

Background:

Assessing predictive accuracy in survival models is crucial.
Various R(2)-type measures exist, but their performance varies.
Previous work (Part I) analyzed explained variation measures.

Purpose of the Study:

To evaluate the performance of remaining R(2)-type measures for survival models.
To identify strengths and weaknesses of these predictive measures.
To compare performance against established criteria using simulations.

Main Methods:

Simulation studies were conducted to assess measure performance.
Measures were evaluated based on criteria established in Part I.
Strengths and shortcomings of each measure were discussed.

Main Results:

The Kent and O'Quigley ρ(W)(2) (and approximation ρ(W,A)(2)) and Schemper and Kaider R(SK)(2) measures demonstrated superior performance.
ρ(W)(2) was found to be sensitive to covariate distribution and influential observations.
Other measures performed poorly due to sensitivity to censoring and follow-up duration.

Conclusions:

ρ(W)(2) and R(SK)(2) are recommended R(2)-type measures for survival model evaluation.
Researchers should consider covariate distribution and data influence when using ρ(W)(2).
The impact of censoring and follow-up time affects the reliability of other measures.