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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...
Cancer Prevention02:59

Cancer Prevention

Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical 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:
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...
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:

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Related Experiment Video

Updated: Jun 14, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Evaluating cancer epidemiologic risk factors using multiple primary malignancies.

Ekatherina Kuligina1, Anne Reiner, Evgeny N Imyanitov

  • 1N N Petrov Institute of Oncology, Russian Federation, St Petersburg, Russia.

Epidemiology (Cambridge, Mass.)
|March 20, 2010
PubMed
Summary

Patients with double primary cancers, like bilateral breast cancer, offer valuable insights for cancer research. Studying these individuals can enhance the power of genetic studies and identify rare, potent risk factors.

Related Experiment Videos

Last Updated: Jun 14, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Area of Science:

  • Cancer Epidemiology
  • Genetic Epidemiology
  • Genetics of Cancer

Background:

  • Patients with double primary malignancies are proposed as a genetically enriched resource for cancer case-control studies.
  • This approach assumes shared risk factors for first and second primary cancers.
  • Statistical power gains depend on similar relative risks in first-time cancer survivors and the general population.

Purpose of the Study:

  • To theoretically and empirically explore the assumptions underlying the use of patients with double primary malignancies in cancer epidemiology.
  • To evaluate the utility of bilateral breast cancer cases in case-control studies.

Main Methods:

  • Literature review of case-control studies on breast cancer risk variants (CHEK2, BRCA1, BRCA2, FGFR2).
  • Obtained summary odds ratios for three study designs: conventional case-control, bilateral vs. unilateral cases, and bilateral vs. population controls.

Main Results:

  • Observed increasing prevalence of risk factors from healthy controls to primary cases to bilateral cases.
  • Relative risks for unilateral breast cancer survivors were similar to or modestly attenuated compared to the general population.

Conclusions:

  • Patients with double primary malignancies represent an underutilized resource for cancer epidemiology.
  • These patients are particularly valuable for genome-wide discovery studies and identifying rare, high-penetrance risk factors.