A1329
Title: Robust estimation of population attributable fractions in the presence of multiple ordered mediators
Authors: Han Chi Peng - Institute of Statistics, National Yang Ming Chiao Tung University (Taiwan) [presenting]
Abstract: Population attributable fraction (PAF) is a key epidemiological measure used to quantify the contribution of risk factors to the overall disease burden. However, when an exposure affects an outcome through multiple ordered mediators, traditional PAF estimation methods face challenges in accurately identifying the impact of each mediating pathway. These challenges arise from mediator-outcome relationships, interactions among mediators, and the presence of potential confounders. New measures, termed mPAFs, are proposed to quantify the fraction of disease attributable to a specific mediation pathway. The proposed framework incorporates a multiply robust estimator yielding consistent mPAF estimates if at least two of the three types of models are correctly specified: exposure, mediator, or outcome models. The asymptotic properties of the estimator are formally established, and simulations demonstrate its robustness against misspecification. In a TCGA lung cancer cohort application, smoking's effect on mortality mediated through TTK and MAD2L1 is analyzed. In lung adenocarcinoma, total PAF was 4.45\%, with a direct effect of 1.82\% and pathway-specific contributions of -1.95\% (TTK) and 0.68\% (MAD2L1). In contrast, lung squamous cell carcinoma showed a 10.43\% total PAF, with most of the effect attributable to the direct pathway (10.22\%), suggesting minimal mediation via the selected genes.
