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A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human reviewers in identifying associations worthy of further investigation (i.e., signals) embedded within a database consisting largely of background 'noise' containing reports of no substantial public health significance. Data mining algorithms are, therefore, being developed, tested and/or used by health authorities, pharmaceutical companies and academic researchers. After a focused review of postapproval drug safety signal detection, the authors explain how the currently used algorithms work and address key questions related to their validation, comparative performance, deployment in naturalistic pharmacovigilance settings, limitations and potential for misuse. Suggestions for further research and development are offered.

Purpose To assess case-only designs for surveillance with administrative databases. Methods We reviewed literature on two designs that are observational analogs to crossover experiments: the self-controlled case series (SCCS) and the case-crossover (CCO) design. Results SCCS views the 'experiment' prospectively, comparing outcome risks in windows with different exposures. CCO retrospectively compares exposure frequencies in case and control windows. The main strength of case-only designs is they entail self-controlled analyses that eliminate confounding and selection bias by time-invariant characteristics not recorded in healthcare databases. They also protect privacy and are computationally efficient, as they require fewer subjects and variables. They are better than cohort designs for investigating transient effects of accurately recorded preventive agents, for example, vaccines. They are problematic if timing of self-administration is sporadic and dissociated from dispensing times, for example, analgesics. They tend to have less exposure misclassification bias and time-varying confounding if exposures are brief. Standard SCCS designs are bidirectional (using time both before and after the first exposure event), so they are more susceptible than CCOs to reverse-causality bias, including immortal-time bias. This is true also for sequence symmetry analysis, a simplified SCCS. Unidirectional CCOs use only time before the outcome, so they are less affected by reverse causality but susceptible to exposure-trend bias. Modifications of SCCS and CCO partially deal with these biases. The head-to-head comparison of multiple products helps to control residual biases. Conclusion The case-only analyses of intermittent users complement the cohort analyses of prolonged users because their different biases compensate for one another. Copyright © 2012 John Wiley & Sons, Ltd. key words-methods; safety monitoring; self-controlled designs; crossover Pharmacoepidemiologists who monitor the safety of medical products using healthcare administrative databases are increasingly interested to know when case-only designs can or cannot be used. To address this question, we (i) defined case-only designs in relation to each other; (ii) examined their main strength: self-controlled comparisons; (iii) discussed the major difference among the designs: directionality; (iv) described the range of medical products assessed with these designs in relation to their susceptibility to exposure misclassification; and (v) made recommendations to safety surveillance programs. DEFINITIONS AND ILLUSTRATIONSThe defining feature of case-only designs is that the analysis is restricted to cases, that is, people who experience at least one outcome event. Also in these designs, each individual serves as his or her own self-matched control, hence the term self-controlled.1-3 The term crossover arises when the analysis is restricted to people who supply both exposed and unexposed person-time and thus 'crossover' between two or more exposure levels. 4,5

Systematic processes for risk identification can provide useful information to supplement an overall safety assessment, but assessment of methods performance suggests a substantial chance of identifying false positive associations.