Related Experiment Video
Updated: Aug 4, 2026
10:00
Measurement of Lifespan in Drosophila melanogaster
Published on: January 7, 2013
Evaluating longevity of composite beef females using survival analysis techniques
P L Rogers1, C T Gaskins, K A Johnson
1Department of Animal Sciences, Washington State University, Pullman 99164-6310, USA.
Journal of Animal Science
|March 23, 2004
Summary
Beef female longevity is influenced by calving ease and genetics for weight and gain, not early life traits. Improving longevity genetically is challenging due to low heritability and lack of early indicators.
Area of Science:
- Animal Science
- Reproductive Biology
- Quantitative Genetics
Background:
- Beef female longevity is crucial for herd profitability.
- Identifying factors influencing longevity and heritability is essential for genetic improvement strategies.
- Culling open cows after a 45-day breeding season provides a specific criterion for functional longevity.
Purpose of the Study:
- To identify risk factors impacting beef female longevity.
- To assess the predictive value of early-life measurements for longevity.
- To estimate the heritability of functional longevity in beef females.
Main Methods:
- Cox regression modeling was used to analyze longevity data from 1,379 Composite Gene Combination (CGC) cows.
- Factors analyzed included age at first calving, calf birth weight, dystocia, breeding values for cow weight, and maternal breeding values for preweaning gain.
- Heritability of functional longevity was estimated.
Main Results:
- Dystocia significantly increased the risk of culling (P < 0.01).
- Higher breeding values for cow weight decreased culling risk (P < 0.01), while increased maternal breeding values for preweaning gain raised culling risk (P < 0.05).
- Early-life traits (before 1 year) did not predict longevity; heritability was estimated at 0.14.
Conclusions:
- Longevity in beef females is influenced by calving ease and genetic factors related to size and growth, but not by early-life measurements.
- The low heritability (0.14) suggests genetic improvement for longevity will be challenging.
- Matching cow genetic potential for size and milk to the environment is key to preventing rebreeding issues and improving retention.
Related Concept Videos
01:18Introduction To Survival Analysis
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
The primary goal of survival analysis is to estimate survival time—the time until a...
01:20Actuarial 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,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:24Kaplan-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,...
01:15Assumptions 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.
01:20Comparing 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...
01:21Cancer 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...