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Related Concept Videos

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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:
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...

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Related Experiment Video

Updated: May 9, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

A conceptual and methodological framework for investigating etiologic heterogeneity.

Colin B Begg1, Emily C Zabor, Jonine L Bernstein

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, U.S.A.

Statistics in Medicine
|July 17, 2013
PubMed
Summary

Molecular tumor profiling reveals distinct subtypes with unique causes, challenging traditional cancer research. This study provides a framework to better identify and analyze these etiologic differences for improved risk prediction.

Keywords:
cancer epidemiologyclusteringetiologic heterogeneity

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Area of Science:

  • Oncology
  • Molecular Genetics
  • Epidemiology

Background:

  • Traditional cancer research uses disease site of origin.
  • Molecular genetics advances reveal tumor subtypes with distinct characteristics.
  • This necessitates investigating distinct etiologies for these subtypes.

Purpose of the Study:

  • To establish a general conceptual and analytic framework for studying etiologic heterogeneity in tumors.
  • To provide efficient strategies for designing and analyzing epidemiologic studies on tumor subtypes.
  • To formally define etiologic heterogeneity and its relation to disease risk predictability.

Main Methods:

  • Proposed a formal definition of etiologic heterogeneity.
  • Outlined analytic strategies for estimating and optimizing etiologic heterogeneity among subtypes.
  • Illustrated concepts using a pooled case-control study of breast cancer subtypes based on gene expression.

Main Results:

  • Classifications of tumor subtypes with greater etiologic heterogeneity show increased disease risk predictability.
  • Demonstrated methods for estimating the degree of etiologic heterogeneity.
  • Identified strategies for selecting subtypes that maximize heterogeneity.

Conclusions:

  • Molecular profiling enables identification of tumor subtypes with distinct etiologies.
  • A formal framework and analytic strategies can improve the study of etiologic heterogeneity.
  • Optimizing subtype classification enhances disease risk predictability and understanding of cancer development.