Partitioning the Population Attributable Fraction for a Sequential Chain of Effects
Author Information
Author(s): Mason Craig A, Tu Shihfen
Primary Institution: University of Maine
Hypothesis
Can a new method for estimating the population attributable fraction (PAF) across multiple risk factors provide clearer and more interpretable measures of effect?
Conclusion
The proposed method offers valuable insights into how population rates of a disorder may be influenced by a sequence of risk factors.
Supporting Evidence
- The proposed method provides clearer measures of effect even when risk factors are correlated.
- It quantifies issues raised by previous researchers regarding population shifts.
- The approach can control for confounding variables without differentiating direct and indirect effects.
Takeaway
This study shows a new way to understand how different risk factors work together to affect health outcomes, making it easier to see their combined impact.
Methodology
The study proposes a method for partitioning the overall PAF into components based on the sequential ordering of effects, applied to hypothetical data sets.
Potential Biases
Potential biases may arise from the assumptions made regarding the independence and interaction of risk factors.
Limitations
The method may not address all complexities of risk factor interactions and assumes a specific causal sequence.
Digital Object Identifier (DOI)
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