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

Chi-square Analysis

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...

Chi-square Distribution

Chi-square Distribution

How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...

Finding Critical Values for Chi-Square

Finding Critical Values for Chi-Square

Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...

Introduction to Test of Independence

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:

Test for Homogeneity

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...

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...

You might also read

Related Articles

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

Sort by

Same author

Correlates of 1-Year Change in Quality of Life in Patients with Urologic Chronic Pelvic Pain Syndrome: Findings from the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network.

The Journal of urology·2020

Same author

Urinalysis in patients with neuromyelitis optica spectrum disorder.

European journal of neurology·2019

Same author

Randomized multicenter clinical trial of myofascial physical therapy in women with interstitial cystitis/painful bladder syndrome and pelvic floor tenderness.

The Journal of urology·2012

Same author

One in four people may develop symptomatic hip osteoarthritis in his or her lifetime.

Osteoarthritis and cartilage·2010

Same author

Variability of creatinine measurements in clinical laboratories: results from the CRIC study.

American journal of nephrology·2010

Same author

Rescoring the NIH chronic prostatitis symptom index: nothing new.

Prostate cancer and prostatic diseases·2009

Search research articles

Related Experiment Video

Updated: Jul 14, 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

A computer program for the generalized chi-square analysis of competing risks grouped survival data (CRISCAT)

W M Stanish, G Y Chi, W D Johnson

Computer Programs in Biomedicine

|September 1, 1978

Summary

CRISCAT is a new computer program for analyzing survival data with competing risks. It uses weighted least squares to adjust survival rates, enabling unified estimation and hypothesis testing.

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

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Related Experiment Videos

Last Updated: Jul 14, 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

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

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Area of Science:

Biostatistics
Survival Analysis
Computational Statistics

Background:

Analyzing grouped survival data with competing risks presents statistical challenges.
Existing methods may not offer a unified approach for estimation and hypothesis testing.
The need for robust computational tools in biostatistics is ongoing.

Purpose of the Study:

To introduce CRISCAT, a novel computer program for analyzing grouped survival data with competing risks.
To provide a unified computational framework linking competing risks theory to linear models.
To facilitate estimation and hypothesis testing of adjusted survival rates.

Main Methods:

Utilizes weighted least squares methods for survival data analysis.
Employs general matrix operations for competing risks adjustments, drawing from GENCAT strategies.
Integrates competing risks theory with linear models for contingency table analysis.

Main Results:

CRISCAT computes adjusted survival rates for multiple causes of failure at various time points.
The program generates functions of adjusted survival rates for fitting asymptotic regression models.
Provides test statistics for hypotheses concerning these functions and model parameters.

Conclusions:

CRISCAT offers a unified computational approach for survival data with competing risks.
The program facilitates robust estimation and hypothesis testing of adjusted survival rates.
Links advanced statistical theory to practical data analysis in biostatistics.