Weighing the causal pies in case-control studies.

Area of Science:

• Epidemiology
• Causal inference
• Public health

Background:

• Rothman's causal pies model is a framework for understanding disease etiology.
• Previous methods like proportion of diseased subjects who develop the disease due to classes of sufficient causes (PDCs) do not directly correspond to causal pie classes.
• There is a need for methods to quantify the contribution of specific causal pathways.

Purpose of the Study:

• To introduce and demonstrate the estimation of "causal-pie weights" (CPWs).
• To assign a unique weight to each class of causal pies within Rothman's framework.
• To provide a method for quantifying the relative importance of different causal pathways.

Main Methods:

• Utilized a non-negative linear odds model constrained to odds ratios (ORs) ≥ 1.
• Applied additive or superadditive interactions between risk factors.
• Calculated population attributable fractions and subsequently CPWs using published case-control data.

Main Results:

• Causal-pie weights (CPWs) were successfully estimated.
• CPWs effectively quantify the relative importance of distinct causal pie classes.
• Demonstrated the methodology using real-world case-control data.

Conclusions:

• The proposed CPW method clarifies complex, multi-factorial disease causation.
• This approach offers valuable insights for designing effective public health intervention strategies.
• Enhances understanding of disease etiology and risk factor interactions.