References
- Akbari A, Razzaghi Z, Homaee F, et al (2011). Parity and breastfeeding are preventive measures against breast cancer in Iranian women. Breast Cancer, 18, 51-5. https://doi.org/10.1007/s12282-010-0203-z
- Chu H, Wang Z, Cole S, et al (2006). Sensitivity analysis of misclassification: A graphical and a Bayesian approach. Ann Epidemiol, 16, 834-41. https://doi.org/10.1016/j.annepidem.2006.04.001
- Ebrahimi M, Vahdaninia M, Montazeri A (2002). Risk factors for breast cancer in Iran: a case-control study. Breast Cancer Res, 4, R10. https://doi.org/10.1186/bcr454
- Fox M, Lash T, Greenland S (2005). A method to automate probabilistic sensitivity analyses of misclassified binary variables. International J Epidemiol, 36, 1370-6.
- Gelman A, Carlin JB, Stern HS, et al 2003. Bayesian Data Analysis, London, CRC Press.
- Gelman A, Rubin DB ( 1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457-511. https://doi.org/10.1214/ss/1177011136
- Ghiasvand R, Bahmanyar S, Zendehdel K, et al (2012). Postmenopausal breast cancer in Iran; risk factors and their population attributable fractions. BMC Cancer, 12, 414. https://doi.org/10.1186/1471-2407-12-414
- Ghiasvand R, Maram ES, Tahmasebi S, et al (2011). Risk factors for breast cancer among young women in southern Iran. International J Cancer, 129, 1443-9. https://doi.org/10.1002/ijc.25748
- Greenland S (2005). Multiple-bias modelling for analysis of observational data. J Royal Statistical Society, A, 168, 1-25. https://doi.org/10.1111/j.1467-985X.2004.00333.x
- Greenland S (2008). Invited commentary: variable selection versus shrinkage in the control of multiple confounders. Am J Epidemiology, 167, 523-9.
- Greenland S (2009). Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods. International J Epidemiol, 38, 1662-73. https://doi.org/10.1093/ije/dyp278
- Greenland S, Lash T (2008). Bias analysis. In 'Modern Epidemiology', Eds Lippincott-Williams-Wilkins, Philadelphia, 345-80
- Greenland S, Mansournia MA (2015). Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Statistics in Medicine, 34, 3133-43. https://doi.org/10.1002/sim.6537
- Hamra G, MacLehose R, Richardson D (2013a). Marcov Chain Monte Carlo: an introduction for epidemiologists. International J Epidemiol, 42, 627-34. https://doi.org/10.1093/ije/dyt043
- Hamra GB, MacLehose RF, Cole SR (2013b). Sensitivity analyses for sparse-data problems-using weakly informative bayesian priors. Epidemiology (Cambridge, Mass.), 24, 233-9. https://doi.org/10.1097/EDE.0b013e318280db1d
- Hassanzadeh J, Moradzadeh R, Rajaee fard A, et al (2012). A comparison of case-control and case-only designs to investigate gene-environment interactions using breast cancer data. Iranian Journal of Medical Sciences, 37, 112-8.
- Holakouie-Naieni K, Ardalan A, Mahmoodi M, et al (2007). Risk factors of breast cancer in North of Iran: A Case-Control in Mazandaran Province. Asian Pac J Cancer Prev, 8, 395-8.
- Hosseinzadeh M, Eivazi Ziaei J, Mahdavi N, et al (2014). Risk factors for breast cancer in Iranian women: a hospitalbased case-control study in tabriz, Iran. J Breast Cancer, 17, 236-43. https://doi.org/10.4048/jbc.2014.17.3.236
- Jurek AM, Lash TL, Maldonado G (2009). Specifying exposure classification parameters for sensitivity analysis: family breast cancer history. Clinical Epidemiology, 1, 109-17.
- Keil AP, Daniels JL, Hertz-Picciotto I (2014). Autism spectrum disorder, flea and tick medication, and adjustments for exposure misclassification: the CHARGE (CHildhood Autism Risks from Genetics and Environment) case-control study. Environmental Health, 13, 3. https://doi.org/10.1186/1476-069X-13-3
- Lash T, Fink A (2003). Semi-automated sensitivity analysis to assess systematic errors in observational data. Epidemiology, 14, 451-8.
- Lash TL, Fox MP, Fink AK 2009. Applying quantitative bias analysis to epidemiologic data, berlin, Germany, Springer.
- Lash TL, Fox MP, MacLehose RF, et al (2014). Good practices for quantitative bias analysis. International Journal of Epidemiology, 43, 1969-85. https://doi.org/10.1093/ije/dyu149
- MacLehose RF, Gustafson P (2012 January). Is probabilistic bias analysis approximately Bayesian? Epidemiology, 23, 151-8. https://doi.org/10.1097/EDE.0b013e31823b539c
- MacLehose RF, Olshan AF, Herring AH, et al (2009). Bayesian Methods for Correcting Misclassification: An Example from Birth Defects Epidemiology. Epidemiology, 20, 27-35. https://doi.org/10.1097/EDE.0b013e31818ab3b0
- Mahouri K, Dehghani-Zahedani M, Zare S (2007 Nov-Dec). Breast cancer risk factors in south of Islamic Republic of Iran: a case-control study. East Mediterr Health J, 13, 1265-73. https://doi.org/10.26719/2007.13.6.1265
- Marrie RA, Cutter G, Tyry T, et al (2008). Smoking status over two years in patients with multiple sclerosis. Neuroepidemiology, 32, 72-9.
- Prescott GJ, Garthwaite PH (2005). Bayesian analysis of misclassified binary data from a matched case-control study with a validation sub-study. Statistics in Medicine, 24, 379-401. https://doi.org/10.1002/sim.2000
- Rothman KJ, Greenland S, Lash TL (2008). Validity in epidemiologic studies. in 'modern epidemiology', eds lippincott williams and wilkins, Philadelphia, 128-47
- Sepandi M, Akrami M, Tabatabaee H, et al (2014). Breast Cancer Risk Factors in Women Participating in a Breast Screening Program: a Study on 11,850 Iranian Females. Asian Pac J Cancer Prev, 15, 8499-502. https://doi.org/10.7314/APJCP.2014.15.19.8499
- Szatmari P, Jones MB (1999). Effects of misclassification on estimates of relative risk in family history studies. Genetic Epidemiology, 16, 368-81. https://doi.org/10.1002/(SICI)1098-2272(1999)16:4<368::AID-GEPI4>3.0.CO;2-A
- Tehranifar P, Wu H-C, Shriver T, et al (2015). Validation of family cancer history data in high-risk families: the influence of cancer site, ethnicity, kinship degree, and multiple family reporters. American Journal of Epidemiology.
- van Gelder MMHJ, Donders ART, Devine O, et al (2014a). Using bayesian models to assess the effects of under-reporting of cannabis use on the association with birth defects, national birth defects prevention study, 1997-2005. Paediatric and Perinatal Epidemiology, 28, 424-33. https://doi.org/10.1111/ppe.12140
- van Gelder MMHJ, Donders ART, Devine O, et al (2014b). Using bayesian models to assess the effects of under-reporting of cannabis use on the association with birth defects, national birth defects prevention study, 1997-2005. Paediatric and Perinatal Epidemiology, n/a-n/a.
- Veisy A, Lotfinejad S, Salehi K, et al (2015). Risk of breast cancer in relation to reproductive factors in north-west of Iran, 2013-2014. Asian Pac J Cancer Prev, 16, 451-5. https://doi.org/10.7314/APJCP.2015.16.2.451
- Zare N, Haem E, Lankarani KB, et al (2013). Breast cancer risk factors in a defined population: weighted logistic regression approach for rare events. J Breast Cancer, 16, 214-9. https://doi.org/10.4048/jbc.2013.16.2.214
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