Subtype-Dependent Relationship Between Young Age at Diagnosis and Breast Cancer Survival

Ann H Partridge1, Melissa E Hughes2, Erica T Warner2

  • 1Ann H. Partridge, Melissa E. Hughes, Erica T. Warner, Eric P. Winer, Jane C. Weeks, and Rulla M. Tamimi, Dana-Farber Cancer Institute and Brigham and Women's Hospital; Erica T. Warner, Harvard T.H. Chan School of Public Health, Boston, MA; Rebecca A. Ottesen and Joyce C. Niland, City of Hope Comprehensive Cancer Center, Duarte; and Douglas W. Blayney, Stanford Cancer Institute, Palo Alto, CA; Yu-Ning Wong, Fox Chase Cancer Center, Philadelphia, PA; Stephen B. Edge, Baptist Cancer Center, Memphis, TN; and Richard L. Theriault, The University of Texas MD Anderson Cancer Center, Houston, TX. ahpartridge@partners.org.

Abstract

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...
811
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...
691
Life Histories01:29

Life Histories

Overview
23.1K
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,...
354
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...
13.6K
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.
473