Related Experiment Video
Updated: Jul 17, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Estimating age-specific breast cancer risks: a descriptive tool to identify age interactions
William F Anderson1, Rayna K Matsuno, Mark E Sherman
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Executive Plaza South 8070, 6120 Executive Plaza Blvd, Rockville, MD 20892-7242, USA. wanderso@mail.nih.gov
Cancer Causes & Control : CCC
|January 12, 2007
Summary
Breast cancer risk factors interact differently with age, with some risks reversing between younger and older women. This suggests distinct causes for early-onset versus late-onset breast cancer, impacting etiological models.
Area of Science:
- Oncology
- Epidemiology
- Genetics
Background:
- Understanding age-specific breast cancer risks is crucial for identifying etiological factors.
- Investigating interactions between risk factors and age can provide deeper insights into breast cancer development.
Purpose of the Study:
- To clarify age-specific female breast cancer risks and their interactions with various risk factors.
- To explore potential etiologic clues derived from age-dependent risk factor effects.
Main Methods:
- A population-based case-control study was conducted in Poland from 2000-2003.
- Data included 2,386 incident breast cancer cases and 2,502 control subjects aged 25-74 years.
- Age-specific incidence rates were estimated in relation to risk factors, examining quantitative and qualitative age interactions.
Main Results:
- Elevated breast cancer risks were associated with positive family history (FH), early menarche, late first birth, nulliparity, hormone use, and low physical activity (PA).
- Significant quantitative and qualitative age interactions were observed for most risk factors, except FH and PA.
- Nulliparity showed a risk reduction in younger women (25-39) but an increased risk in older women (40-74), demonstrating a qualitative age interaction.
Conclusions:
- Qualitative age interactions observed were counterintuitive and challenge current stochastic breast cancer models.
- The reversal of relative risks suggests potential subgroup heterogeneity, with different etiologic mechanisms for early- and late-onset breast cancer.
- Further validation in diverse populations is needed to confirm these qualitative interactions and their implications for breast cancer etiology.
Related Concept Videos
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...
01:25Statistical Methods for Analyzing Epidemiological Data
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
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,...