Cox model and decision trees: an application to breast cancer data
Lucas Cardoso Pereira1, Sóstenes Jerônimo da Silva2, Cleanderson Romualdo Fidelis3
1Rural Federal University of Pernambuco Recife Brazil Rural Federal University of Pernambuco, Recife, Brazil.
Summary
Triple-negative breast cancer patients face shorter survival. Hormone therapy and HER2-positive status significantly improve survival outcomes, highlighting potential for targeted treatments.
Area of Science:
- Oncology
- Biostatistics
Background:
- Breast cancer is a leading cause of mortality in women.
- Understanding prognostic factors is crucial for improving patient outcomes.
Purpose of the Study:
- To analyze survival data in breast cancer patients using survival analysis techniques.
- To identify key covariates influencing time to death in breast cancer.
- To assess the discriminatory power of conditional inference trees for breast cancer prognosis.
Main Methods:
- Retrospective cohort study of 10-year breast cancer patient data (2005-2015).
- Survival curves estimated via Kaplan-Meier method and Cox regression.
- Conditional decision trees utilized for prognostic discrimination.
Main Results:
- Triple-negative breast cancer subtypes showed significantly shorter survival.
- Hormone therapy reduced mortality risk by 5.5%.
- HER2-positive patients had a 34.5% lower risk of death compared to HER2-negative patients.
Conclusions:
- Immunotherapy presents a favorable outlook for HER2-overexpressing breast cancer.
- Further research on immunotherapy efficacy in diverse patient groups is warranted.
- Optimizing treatment strategies can enhance prognosis and quality of life.
More Related Videos
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
400
07:41Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
9.1K
Related Concept Videos
01:21Cancer Survival Analysis
472
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...
472
01:19Survival Tree
172
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
172
01:20Comparing the Survival Analysis of Two or More Groups
321
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...
321
01:19The Mantel-Cox Log-Rank Test
607
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
607
02:43Mouse Models of Cancer Study
5.8K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
5.8K
01:24Kaplan-Meier Approach
288
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,...
288