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Related Concept Videos

Causality in Epidemiology01:21

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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...
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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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Causal inference for oncology: past developments and current challenges.

Erica E M Moodie1

  • 1Department of Epidemiology & Biostatistics, McGill University, Montréal, Québec, Canada.

The International Journal of Biostatistics
|September 2, 2022
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Summary
This summary is machine-generated.

This review explores early causal inference methods in oncology, highlighting their evolution and current challenges. It examines foundational examples and discusses adaptive treatment strategies for censored outcomes.

Keywords:
counterfactualshealthy worker effectoccupational exposurespotential outcomesprecision medicine

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Area of Science:

  • Medical statistics
  • Epidemiology
  • Oncology

Background:

  • Causal inference methods are crucial in medical research.
  • Early developments were significantly influenced by oncology research questions.

Purpose of the Study:

  • To review key early developments in causal inference within medical statistics and epidemiology.
  • To examine classical examples and identify current methodological advancements.
  • To highlight ongoing challenges in causal inference, especially with censored outcomes in oncology.

Main Methods:

  • Literature review of early causal inference developments.
  • Examination of two classical case studies in oncology.
  • Discussion of optimal adaptive treatment strategies.

Main Results:

  • Causal inference approaches are increasingly standard in oncology.
  • Classical examples illustrate foundational concepts.
  • Optimal adaptive treatment strategy estimation is an active research area.

Conclusions:

  • Significant progress has been made in applying causal inference to oncology.
  • Challenges remain, particularly for analyzing censored data.
  • Further methodological development is needed for complex clinical scenarios.