Related Experiment Video

Updated: Jun 24, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

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

Published on: September 27, 2024

Racial differences in the impact of comorbidities on survival among elderly men with prostate cancer

Mary Putt1, Judith A Long, Chantal Montagnet

  • 1Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia 19104, USA.

Medical Care Research and Review : MCRR
|April 10, 2009
PubMed

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer07:25

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer

Published on: March 6, 2018

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups01:20

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 Analysis01:21

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...
Disorders of the Male Reproductive System01:20

Disorders of the Male Reproductive System

Men's health issues are increasingly recognized as significant, with several conditions posing common threats. Among these, testicular cancer is especially prevalent in younger men, particularly those aged 20 to 35 years. The disease often manifests as a painless mass in the testicles, sometimes accompanied by a sensation of heaviness or a dull ache.
Prostate disorders are another major concern. These conditions can impair urinary flow due to the prostate's location around the urethra. Symptoms...
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Assumptions of Survival Analysis01:15

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.