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

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...

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...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

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,...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

You might also read

Related Articles

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

Sort by

Same author

Multidimensional sampling framework reveals plant-driven effects on microbial spatial heterogeneity and niche differentiation in a natural ecosystem.

Environmental microbiome·2025

Same author

Integrated Use of Late Gadolinium Enhancement and Left Ventricular Global Longitudinal Strain in Hypertrophic Cardiomyopathy.

JACC. Asia·2025

Same author

The Effect of Mavacamten on Left Atrial Strain Dynamics in Obstructive Hypertrophic Cardiomyopathy.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography·2025

Same author

Freshwater shrimp (Neocaridina denticulata) as a nature-based restoration tool for macrophyte recovery and improved water quality in eutrophic ponds.

Journal of environmental management·2025

Same author

Adverse drug reactions during nontuberculous mycobacterial pulmonary disease treatment: a systematic review and meta-analysis.

Annals of the American Thoracic Society·2025

Same author

A self-heating, multi-channel slider cassette for innovative point-of-care molecular diagnostics from whole blood samples.

Biosensors & bioelectronics·2025

Search research articles

Related Experiment Video

Updated: May 15, 2026

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

Selecting cases for whom additional tests can improve prognostication.

Xiaoqian Jiang¹, Jihoon Kim, Yuan Wu

¹Division of Biomedical Informatics, Department of Medicine University of California at San Diego, La Jolla, CA 92093, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|January 11, 2013

Summary

This summary is machine-generated.

Identifying patients for additional testing is crucial. Gene expression data can improve prognostic models for older breast cancer patients with large tumors and positive lymph nodes.

Related Experiment Videos

Last Updated: May 15, 2026

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:

Biostatistics
Oncology
Genomics

Background:

Prognostic models are essential in clinical practice for patient management.
Current methods often assess the benefit of additional variables on entire patient cohorts.
However, additional information may only improve model accuracy for specific patient subgroups.

Purpose of the Study:

To develop a method for identifying patient subsets who benefit from additional prognostic testing.
To determine if gene expression measurements enhance prognostic model accuracy for specific breast cancer patient groups.

Main Methods:

The study proposes a novel method to pinpoint patients who may gain the most from further diagnostic tests.
Utilized a dataset of breast cancer cases to evaluate the proposed method.

Main Results:

Experiments indicated that older patients with larger tumors and positive lymph nodes represent a subgroup whose prognoses are more accurately predicted with gene expression data.
The addition of gene expression measurements did not yield similar benefits for all patient subgroups.

Conclusions:

A targeted approach to incorporating additional data, like gene expression, can optimize prognostic model utility.
This method aids in prioritizing patients for further testing, leading to more personalized and accurate prognoses.