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,...

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.

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...

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...

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...

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,...

You might also read

Related Articles

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

Sort by

Same author

Cost-Effectiveness of Metoclopramide, Paracetamol, and Ceftriaxone for the Prevention of Infections and Fever in Elderly Patients with Acute Stroke.

MDM policy & practice·2026

Same author

General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations.

Medical decision making : an international journal of the Society for Medical Decision Making·2023

Same author

An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial.

Medical decision making : an international journal of the Society for Medical Decision Making·2021

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 12, 2025

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

Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.

Mathyn Vervaart^1,2

¹Department of Health Management and Health Economics, University of Oslo, Oslo, Norway.

Medical Decision Making : an International Journal of the Society for Medical Decision Making

|September 21, 2024

Summary

Quantify the value of new data using the expected net benefit of sampling (ENBS). This tutorial provides an R algorithm for calculating ENBS with survival data, aiding study design and healthcare reimbursement decisions.

Keywords:

economic evaluation model expected net benefit of sampling expected value of sample information simulation methods survival data value of information

More Related Videos

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

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

Related Experiment Videos

Last Updated: Jun 12, 2025

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

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

Area of Science:

Health economics
Biostatistics
Decision science

Background:

Decision uncertainty is common in healthcare, impacting study design and reimbursement.
Quantifying the value of additional data is crucial for efficient resource allocation.
Existing methods for calculating the value of sample information (VSI) often have limitations.

Purpose of the Study:

To present a general-purpose algorithm for computing the expected net benefit of sampling (ENBS) for survival data.
To provide a step-by-step implementation of the ENBS algorithm in R.
To demonstrate the utility of ENBS in optimizing study design and reimbursement decisions.

Main Methods:

The algorithm utilizes recent methods for simulating survival data without assuming specific parametric distributions.
It incorporates arbitrary censoring processes, enhancing applicability.
The calculation of ENBS is based on the expected value of sample information (EVSI).

Main Results:

A novel, general-purpose algorithm for ENBS calculation with survival data is presented.
The R implementation allows for practical application of the algorithm.
Case study demonstrates ENBS utility for designing new studies and optimizing reimbursement for health technologies.

Conclusions:

ENBS is a valuable metric for quantifying the expected value of additional data in healthcare decision-making.
The presented algorithm and R implementation provide a flexible tool for researchers and policymakers.
ENBS supports evidence-based decisions regarding study design and reimbursement, especially with immature evidence.