The validity of the method has been demonstrated. It detects existing problems; it finds new signals and prioritises them for the benefit of assessors; it is very simple, transparent, and objective; and it can be automated very easily indeed.
The ''Bayesian Data Mining'' approach used by the FDA (DuMouchel, 1999) is very similar, but offers a better statistical analysis when very small numbers are involved. It emphasises ranking of the equivalent of the logarithm of PRR. It uses an "Empirical Bayes'' method, which shrinks log (O/E) towards zero, and the shrinkage is important if E is small, but gives very similar results when observed or expected numbers of reactions are reasonably large. It is slightly more complex in the calculations, and consequently less transparent.
The WHO has a new approach (Bate et al., 1998) based on a Bayesian confidence propaga tion neural network, but again is very similar to a PRR. It uses the log (to the base 2) of the PRR based on the same 2 x 2 table as used with PRRs. Its use of Bayes' theorem in a 2 x 2 table is not controversial and does not incorporate prior belief. The cut-off for a signal is based on the confidence interval around their statistic. The method has the ability to scan the whole database relatively rapidly, forming all tables for combinations of drugs and reactions that occur together and is used routinely.
The major issues are the potential for misinterpretation of the signals and over-reliance on automation. The statistical methods are a first stage of assessment and careful evaluation using medical scientific knowledge is still required. At the same time, the potential contributions of statistical methods and of statisticians have not been fully realised. The advances in the past few years seem to have been greater than in the previous 20 years, although it is recognised that there has been some re-invention. Further statistical creativity is possible, particularly in integrating spontaneous reporting with epidemiological methods and randomised trial data.
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