adverse event reports would also allow one to identify characteristics of a device user (e.g., gender, age, or disease severity) that might affect the probability of an adverse event.1 Quantitatively, for adverse events (AE) that are clearly identifiable as being due to a device, the two probabilities (P) of interest (where Z is an individual characteristic) are
P[AE | device], and
P[AE | device, Z].
In contrast, for adverse events that could be due to the device or other causes (e.g., stroke in persons with aortic valve replacement; cf Ionescu et al., 2003), interest would more naturally focus on probabilities indicating the difference in risk for those with the device and those without, that is,
P[AE | device] versus P[AE | no device], and
P[AE | device, Z] versus P[AE | no device, Z].
As described below, different types of data structures would give rise to different strategies for comparing risks between individuals who do and do not have the device.
A full evaluation of the possible harms posed by a device should put harms in context. Thus, it should include information about the comparative probabilities of desired outcomes or benefit (e.g., reduced mortality, improved hearing, or cessation of tremor).
While the above probabilities represent the quantities we ideally would like to have to evaluate the safety of a device, the statistical association between use of a device and the probability of an adverse event or other outcome can be quantified as a function of these probabilities. One frequently used statistic, the odds ratio (OR), is a measure of the association between a device and an outcome, defined by
Thus, an odds ratio of 1 corresponds to no association between use of the device and the probability of an adverse event, while an odds ratio greater