Effect of Smoking on Breast Cancer by Adjusting for Smoking Misclassification Bias and Confounders Using a Probabilistic Bias Analysis Method
Received 1 March 2020
Accepted for publication 11 May 2020
Published 28 May 2020 Volume 2020:12 Pages 557—568
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Irene Petersen
Reza Pakzad,1 Saharnaz Nedjat,1 Mehdi Yaseri,1 Hamid Salehiniya,2 Nasrin Mansournia,3 Maryam Nazemipour,4 Mohammad Ali Mansournia1
1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; 2School of Public Health, Birjand University of Medical Sciences, Birjand, South Khorasan, Iran; 3Department of Endocrinology, AJA University of Medical Sciences, Tehran, Iran; 4Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran
Correspondence: Mohammad Ali Mansournia
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Tel/Fax +21 88989127
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.
Keywords: probabilistic bias analysis, smoking, breast cancer, Monte Carlo sensitivity analysis, population attributable fraction
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