Effect of Smoking on Breast Cancer by Adjusting for Smoking Misclassification Bias and Confounders Using a Probabilistic Bias Analysis Method

摘要

Purpose: The aim of this study was to determine the association between smoking and breast cancer after adjusting for smoking misclassification bias and confounders. Methods: In this case–control study, 1000 women with breast cancer and 1000 healthy controls were selected. Using a probabilistic bias analysis method, the association between smoking and breast cancer was adjusted for the bias resulting from misclassification of smoking secondary to self-reporting as well as a minimally sufficient adjustment set of confounders derived from a causal directed acyclic graph (cDAG). Population attributable fraction (PAF) for smoking was calculated using Miettinen’s formula. Results: While the odds ratio (OR) from the conventional logistic regression model between smoking and breast cancer was 0.64 (95% CI: 0.36– 1.13), the adjusted ORs from the probabilistic bias analysis were in the ranges of 2.63– 2.69 and 1.73– 2.83 for non-differential and differential misclassification, respectively. PAF ranges obtained were 1.36– 1.72% and 0.62– 2.01% using the non-differential bias analysis and differential bias analysis, respectively. Conclusion: After misclassification correction for smoking, the non-significant negative-adjusted association between smoking and breast cancer changed to a significant positive-adjusted association.