Related Experiment Video

Updated: Oct 5, 2026

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil
06:48

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil

Published on: July 29, 2020

[Inequality of workers' survival in Botucatu, São Paulo State, Brazil]

R Cordeiro1, Silva Ed

  • 1Departamento de Saúde Pública, Faculdade de Medicina de Botucatu, Universidade Estadual Paulista, Botucatu, SP, 18618-970, Brasil.

Cadernos De Saude Publica
|August 22, 2001
PubMed

More Related Videos

Exploring the Longissimus Muscle: Unraveling its Correlation with Meat Quality in Bos indicus and Crossbred Bulls07:46

Exploring the Longissimus Muscle: Unraveling its Correlation with Meat Quality in Bos indicus and Crossbred Bulls

Published on: July 12, 2024

Captive Maintenance and Venom Extraction of Tityus serrulatus (Brazilian Yellow Scorpion) for Antivenom Production05:27

Captive Maintenance and Venom Extraction of Tityus serrulatus (Brazilian Yellow Scorpion) for Antivenom Production

Published on: October 6, 2023

Related Concept Videos

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.
Quantifying Work02:30

Quantifying Work

As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system.
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...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Kaplan-Meier Approach01:24

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,...
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...