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

Abstract

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.

• Authors: Reza Pakzad, Saharnaz Nedjat, Mehdi Yaseri, Hamid Salehiniya, Nasrin Mansournia, Maryam Nazemipour, Mohammad Ali Mansournia
• Journal: Clinical epidemiology (Clin Epidemiol) Vol. 12 Pg. 557-568 ( 2020) ISSN: 1179-1349 [Print] New Zealand
• PMID: 32547245 (Publication Type: Journal Article)
• Copyright: © 2020 Pakzad et al.