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Attributable fractions for sufficient cause interactions.

Tyler J VanderWeele1

  • 1Harvard University, Boston, MA, USA.

The International Journal of Biostatistics
|March 23, 2010
PubMed
Summary
This summary is machine-generated.

This study provides methods to calculate attributable fractions for disease causes, offering insights into disease etiology and prevention strategies. It details calculations for etiologic and excess fractions, with and without monotonicity assumptions.

Keywords:
attributable fractioninteractionmarginal structural modelssufficient causesynergism

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Understanding disease causes is crucial for public health interventions.
  • Attributable fractions quantify the proportion of disease linked to specific causes.

Purpose of the Study:

  • To present methods for calculating attributable fractions for sufficient cause interactions.
  • To compare different approaches for estimating etiologic and excess fractions.

Main Methods:

  • Derivation of exact formulas for excess fractions under monotonicity assumptions.
  • Development of methods to estimate lower bounds for attributable fractions.
  • Utilizing marginal structural models for estimation in the presence of time-dependent confounding.

Main Results:

  • Exact formulas are provided for excess fractions when monotonicity holds.
  • Lower bounds are established for etiologic fractions and excess fractions without monotonicity.
  • A method for estimating these lower bounds using marginal structural models is described.

Conclusions:

  • The study offers a comprehensive framework for quantifying disease burden attributable to specific causes.
  • The methods are applicable even in complex scenarios with time-dependent confounding.
  • Results aid in prioritizing public health interventions and understanding disease causality.