Mortality trends of breast and cervical cancer in Passo Fundo, Rio Grande do Sul: an analysis by age and schooling,

Related Experiment Video

Updated: Aug 14, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Vanessa Pecinato1, Andréia Jacobo1, Shana Ginar da Silva2

  • 1Universidade Federal da Fronteira Sul, Curso de Medicina, Passo Fundo, RS, Brasil.

Epidemiologia E Servicos De Saude : Revista Do Sistema Unico De Saude Do Brasil
|January 11, 2023
PubMed
Abstract

More Related Videos

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil06:48

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil

Published on: July 29, 2020

4.6K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery06:46

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

Published on: September 27, 2024

322

Related Concept Videos

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...
411
Applications of Life Tables01:22

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
102
Actuarial Approach01:20

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,...
109
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.1K
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...
247
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.
172