Signal detection in the pharmaceutical industry: integrating clinical and computational approaches.

Drug safety profiles are dynamic and established over time using multiple, complimentary datasets and tools. The principal concern of pharmacovigilance is the detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency as soon as possible with minimum patient exposure. A key step in the process is the detection of 'signals' that direct safety reviewers to associations that might be worthy of further investigation. Although the 'prepared mind' remains the cornerstone of signal detection safety reviewers seeking potential signals by scrutinising very large, sparse databases may find themselves 'drowning in data but thirsty for knowledge'. Understandably, health authorities, pharmaceutical companies and academic centres are developing, testing and/or deploying computer-assisted database screening tools (also known as data-mining algorithms [DMAs]) to assist human reviewers. The most commonly used DMAs involve disproportionality analysis that project high-dimensional data onto two-dimensional (2 x 2) contingency tables in the context of an independence model. The objective of this paper is to extend the discussion of the evaluation, potential utility and limitations of the commonly used DMAs by providing a 'holistic' perspective on their use as one component of a comprehensive suite of signal detection strategies incorporating clinical and statistical approaches to signal detection -- a marriage of technology and the 'prepared mind'. Data-mining exercises involving spontaneous reports submitted to the US FDA will be used for illustration. Potential pitfalls and obstacles to the acceptance and implementation of data mining will be considered and suggestions for future research will be offered.