Statistical Techniques for Signal Generation

National voluntary reporting systems generate large volumes of clinical data pertinent to drug safety. Currently descriptive statistical techniques are used to assist in the detection of drug safety ‘signals’. Australian data have been coded according to guidelines formulated almost 30 years ago and which have resulted in many drugs which are not associated with an adverse drug reaction or ‘innocent bystander’ drugs being recorded as ‘suspected’ in individual reports. In this paper we explore the application of an iterative probability filtering algorithm titled ‘PROFILE’. This serves to identify the ‘signals’ and remove the ‘innocent bystander’ drugs, thus providing a clearer view of the drugs most likely to have caused the reactions. Reaction terms analysed include neutropenia, agranulocytosis, hypotension, hypertension, myocardial infarction, neuroleptic malignant syndrome, and rectal haemorrhage. In this version of PROFILE, Fishers exact test has been used as the statistical tool but other methods could be used in future. Advantages and limitations of the method and its assumptions are discussed together with the rationale underlying the method and suggestions for further enhancements.