Misclassification Adjustment of Family History of Breast Cancer in a Case-Control Study: a Bayesian Approach

  • Moradzadeh, Rahmatollah (Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences) ;
  • Mansournia, Mohammad Ali (Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences) ;
  • Baghfalaki, Taban (Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University) ;
  • Ghiasvand, Reza (Oslo Center for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo) ;
  • Noori-Daloii, Mohammad Reza (Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences) ;
  • Holakouie-Naieni, Kourosh (Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences)
  • Published : 2016.01.11


Background: Misreporting self-reported family history may lead to biased estimations. We used Bayesian methods to adjust for exposure misclassification. Materials and Methods: A hospital-based case-control study was used to identify breast cancer risk factors among Iranian women. Three models were jointly considered; an outcome, an exposure and a measurement model. All models were fitted using Bayesian methods, run to achieve convergence. Results: Bayesian analysis in the model without misclassification showed that the odds ratios for the relationship between breast cancer and a family history in different prior distributions were 2.98 (95% CRI: 2.41, 3.71), 2.57 (95% CRI: 1.95, 3.41) and 2.53 (95% CRI: 1.93, 3.31). In the misclassified model, adjusted odds ratios for misclassification in the different situations were 2.64 (95% CRI: 2.02, 3.47), 2.64 (95% CRI: 2.02, 3.46), 1.60 (95% CRI: 1.07, 2.38), 1.61 (95% CRI: 1.07, 2.40), 1.57 (95% CRI: 1.05, 2.35), 1.58 (95% CRI: 1.06, 2.34) and 1.57 (95% CRI: 1.06, 2.33). Conclusions: It was concluded that self-reported family history may be misclassified in different scenarios. Due to the lack of validation studies in Iran, more attention to this matter in future research is suggested, especially while obtaining results in accordance with sensitivity and specificity values.


Misclassification;bias;Bayesian assessment;self-report


Supported by : Tehran University of Medical Sciences


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