Pylak Maciej, Fornalski Krzysztof Wojciech, Reszczyńska Joanna, Kukulski Piotr, Waligórski Michael P R, Dobrzyński Ludwik
National Centre for Nuclear Research (NCBJ), Otwock-Świerk, Poland.
Institute of Physics, Polish Academy of Sciences (IF PAN), Warszawa, Poland.
Dose Response. 2021 May 17;19(2):15593258211009337. doi: 10.1177/15593258211009337. eCollection 2021 Apr-Jun.
Three statistical methods: Bayesian, randomized data binning and Maximum Entropy Method (MEM) are described and applied in the analysis of US radon data taken from the US registry. Two confounding factors-elevation of inhabited dwellings, and UVB (ultra-violet B) radiation exposure-were considered to be most correlated with the frequency of lung cancer occurrence. MEM was found to be particularly useful in extracting meaningful results from epidemiology data containing such confounding factors. In model testing, MEM proved to be more effective than the least-squares method (even via Bayesian analysis) or multi-parameter analysis, routinely applied in epidemiology. Our analysis of the available residential radon epidemiology data consistently demonstrates that the relative number of lung cancers decreases with increasing radon concentrations up to about 200 Bq/m, also decreasing with increasing altitude at which inhabitants live. Correlation between UVB intensity and lung cancer has also been demonstrated.
贝叶斯方法、随机数据分箱法和最大熵方法（MEM），并将其应用于分析从美国登记处获取的美国氡数据。两个混杂因素——居住房屋的海拔高度和紫外线B（UVB）辐射暴露——被认为与肺癌发生频率的相关性最强。结果发现，最大熵方法在从包含此类混杂因素的流行病学数据中提取有意义的结果方面特别有用。在模型测试中，最大熵方法被证明比流行病学中常规应用的最小二乘法（即使通过贝叶斯分析）或多参数分析更有效。我们对现有住宅氡流行病学数据的分析一致表明，肺癌的相对数量随着氡浓度增加至约200贝克勒尔/立方米而减少，也随着居民居住海拔高度的增加而减少。紫外线B强度与肺癌之间的相关性也得到了证实。
