Observational epidemiological studies are classified as ecological, case—control, or cohort.
In ecologic epidemiological studies, data on populations, rather than data on individuals, are compared. An example of an ecologic study is the evaluation of geographic areas with high-background radiation levels compared with areas with “normal” background levels. Ecological studies aggregate data over a population in a particular area. Ecological studies are subject to problems of correlations between aggregated disease rates and aggregated measures of exposure. Ecological studies compare average exposure with average cancer risk. Advantages of ecological studies are: (1) they are easy and inexpensive; (2) they can document the frequency of disease over time; and (3) they usually include a large population. A good ecological study adequately controls for confounding factors, and has geographic areas with adequate numbers of dose measurement, small variability of dose within individual geographic regions relative to variability in other regions, availability of high-quality health data across geographic regions, and relatively stable populations [7].
Cohort and case—control studies use data for individuals. Case—control studies compare radiation exposure in individuals with cancer and without cancer. In case—control studies, individuals with a specific cancer are compared with a control group of individuals without the cancer with respect to their past exposure to radiation. Case—control studies are usually not used in radiation epidemiology, with the exception for studies of indoor radon and lung cancer. Case—control studies are susceptible to biases of appropriate selection of controls and valid retrospective determination of dose [7].
There are many ways to skin a cat and the cat does not like any of them (Unknown)
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Crump KS (2006) The effect of random error in exposure measurement upon the shape of the exposure response. Dose-Response 3:456–464
Calabrese EJ, Baldwin LA (2002) Hormesis and high-risk groups. Reg Toxicol Pharmacol 35:414–428
Calabrese EJ, Staudenmayer JW, Stanek EJ, Hoffmann GR (2006) Hormesis outperforms threshold model in NCI anti-tumor drug screening database. Toxicol Sci 94:368–378
Elliott KC (2008) Hormesis, ethics, and public policy: an overview. BELLE Newsletter 14:48–50
Vrijheid M, Cardis E, Blettner M et al (2007) The 15-country collaborative study of cancer risk among radiation workers in the nuclear industry: design, epidemiological methods and descriptive results. Radiat Res 167:361–379
Scott BR, Sanders CL, Mitchel REJ, Boreham DR (2008) CT scans may reduce rather than increase the risk of cancer. J Am Phys Surg 13:7–10
Bennett B, Repacholi M, Carr Z (eds) (2006) Health effects of the Chernobyl accident and special health care programmes. World Health Organization, Geneva, p 5
Tubiana M (2008) The linear no-threshold relationship and advances in our understanding of carcinogenesis. Int J Low Rad 5:173–204
Scott BR (2008) It's time for a new low-dose-radiation risk assessment paradigm—one that acknowledges hormesis. Dose-Response 5:333–351
Rosario AS, Wellmann J, Heid IM et al (2006) Radon epidemiology: continuous and categorical trend estimators when the exposure distribution is skewed and outliers may be present. J Toxicol Environ Health A 69:681–700
Cardis E, Vrijheid M, Blettner M et al (2007) The 15-country collaborative study of cancer risk among radiation workers in the nuclear industry: estimates of radiation-related cancer risks. Radiat Res 167:396–416
Puskin JS (2008) What can epidemiology tell us about risks at low doses? Radiat Res 169:122–124
Sanders CL (2006) Hormesis as a confounding factor in epidemiological studies of radiation carcinogenesis. Korean Assoc Radiat Prot 31:69–89
Sanders CL, Scott BR (2008) Smoking and hormesis as confounding factors in radiation pulmonary carcinogenesis. Dose-Response, 6:53–79
Ioannidis JP, Haidich AB, Lau J (2001) Any casualties in the clash of randomized and observational evievidence? BMJ 322:879–880
Ioannidis JP (2005) Why most published research findings are false. PLOS Medicine 2(8): (http://medicine.plosjournals.org/perlserv?request=get-document&doi=10.1371/journal.pm
Vandenbroucke JP (2004) When are observational studies as credible as randomized trials? Lancet 363:1728–1731
Wacholder S, Chanock S, Garcia-Closas M et al (2004) Assessing the probability that a positive report is false: An approach for molecular epidemiological studies. J Natl Cancer Inst 96:434–442
Topol EJ (2004) Failing the public health-Rofecoxib, Merck, and the FDA. N Engl J Med 351:1707–1709
Stroup DF, Berlin JA, Morton SC et al (2000) Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of Observational Studies in Epidemiology (MOOSE) group. JAMA 283:2008–2012
Krimsky S, Rothenberg LS, Stott P, Kyle G (1998) Scientific journals and their authors' finan-cial interests: a pilot study. Psychther Psychsom 67:194–201
Antman EM, Lau J, Kupelnick B et al (1992) A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts. Treatments for myocardial infarction. JAMA 268:240–248
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(2010). Biased Epidemiological Studies. In: Sanders, C.L. (eds) Radiation Hormesis and the Linear-No-Threshold Assumption. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03720-7_7
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