Analysis of Indoor Radon Data Using Bayesian, Random Binning, and Maximum Entropy Methods

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(3), also decreasing with increasing altitude at which inhabitants live. Correlation between UVB intensity and lung cancer has also been demonstrated.

Automated summary

This study applied three statistical methods - Bayesian, randomized data binning, and Maximum Entropy Method - to analyze US radon data and found that MEM was particularly effective in extracting meaningful results from data containing confounding factors. The analysis consistently demonstrated a decrease in the relative number of lung cancers with increasing radon concentrations and altitude of inhabited dwellings, while also showing a correlation between UVB intensity and lung cancer occurrence.