Subject de-biasing of data sets: A Bayesian approach

In this paper, the authors examine the relevance of data sets (for instance, of past incidents) for risk management decisions when there are reasons to believe that all types of incidents have not been reported at the same rate. Their objective is to infer from the data reports what actually happened in order to assess the potential benefits of different safety measures. The authors use a simple Bayesian model to correct (de-bias) the data sets given the nonreport rates, which are assessed (subjectively) by experts and encoded as the probabilities of reports given different characteristics of the events of interest. They compute a probability distribution for the past number of events given the past number of reports. They illustrate the method by the cases of two data sets: incidents in anesthesia in Australia, and oil spills in the Gulf of Mexico. In the first case, the de-biasing allows correcting for the fact that some types of incidents, such as technical malfunctions, are more likely to be reported when they occur than anesthetist mistakes. In the second case, the authors have to account for the fact that the rates of oil spill reports indifferent incident categories have increased over the years, perhaps at the same time as the rates of incidents themselves