RAMAS® Risk Imaging
Medical imaging technologies such as MRI, ultrasound, and computed tomography have revolutionized medicine. We believe that risk analysts, regulators, decision makers, and the public would benefit if analogous imaging techniques were available to penetrate the cloud of uncertainty and disagreement surrounding risk data. RAMAS® Risk Imaging software provides visualizations of risk in the face of uncertainty regarding the frequency of adverse events and of uncertainty regarding the severity of adverse events.
Psychometric and socio-cultural theories of risk perception emphasize the disparity between expert risk assessments, which focus on the frequency of adverse events of measured magnitudes, and lay assessments, which are conditioned by additional qualities of the hazard and of the risk perceiver. The RAMAS Risk Imaging approach treats this disparity as a form of uncertainty and employs methods to bound variability and incertitude in risk assessments.
Risk is perceived differently by different individuals and interest groups. RAMAS Risk Imaging visualizes risk by quantifying attitudes regarding the importance of uncertainty, the meaning of disagreements between measurements or opinions, and the meaning of absence of evidence. Visualizations of risk are generated for different risk perceivers. Comparing and contrasting these visualizations facilitates communication and decision making.
Although the development of this software was guided by theory formulated in risk perception and risk communication research, the method is theoretically eclectic and meant to be adaptable to a wide range of applications and levels of analysis. The commonality expected across applications is the need of risk analysts and decision makers to convert highly uncertain measurements of the frequency and adversity of multiple harms associated with a potential hazard into an image of risk as variably perceived by different individuals and interest groups.
Risk perception, as conceived in this sense, is unlike an MRI image in that there is no “correct” perception to be recorded. Occupational, environmental, and health risks are experienced and perceived in the context of culturally complex and highly politicized arenas. An analysis informed and colored by these complexities is required. RAMAS Risk Imaging is intended as an aid in the production of uncertainty-informed risk assessment.
RAMAS Risk Imaging requires data regarding the frequency and the adversity of each harm due to a hazard. This data may come from various sources, including controlled trials, surveys, and expert opinions and judgments. Each of these sources results in an uncertain estimate of the true frequency and/or adversity of the harms that comprise a hazard. RAMAS Risk Imaging uses both the uncertain estimates and the uncertainty associated with them to generate an image of risk.
The RAMAS Risk Imaging approach follows four steps in the visualization of risk. The first step is to decompose the risk into it's frequency and adversity components. The next step is to incorporate quantitative uncertainty into these components using methods such as interval arithmetic, info-gap, ordination based upon revealed preferences, and dependency bounding. The third step is to re-compose the risk as a function of uncertain frequency and uncertain adversity. The final step is to focus the risk image on particular risk perceptions by specifying attitudes towards risk and uncertainty.
Example: Risk from hypertension drugs
The easiest way to understand risk visualizations in RAMAS Risk Imaging is to do an example. We will visualize the risk of side effects from a commonly used hypertension drug. This drug was chosen because clinical trail data was easily and publicly available. The analyses presented are intended to illustrate the risk imaging method only and should not be construed as a risk analysis. In a clinical drug trial, patients are given either the drug or a placebo. Side effects are reported and their frequencies tallied. This sort of risk assessment is shown in the table below for the hypertension drug benazepril, as reported in the publicly available Physician’s Desk Reference (PDR 1993).
The severity of each of these adverse reactions must be ranked. We conducted an ad hoc survey which asked a group of people to rank each adverse reaction on a scale of 1 to 7, where 1 is very low severity and 7 is very high severity. The results are shown in the table below.
Uncertain adversity and uncertain frequency of seven adverse reactions to benazepril are shown below. The dashed black lines forming a box show bounds around frequency and adversity combinations for headache that are consistent with the uncertain data. Note that the vertical axis is truncated at 0.1 for the purpose of display.
Next, we combine the risks of each harm, allowing for any and all possible forms of dependence between them, to produce a risk profile of the drug. The figure below shows the risk profile.
Finally, we can focus this visualization of risk to examine the risk perceptions of individuals with different attitudes towards risk and uncertainty. The attitude control panel is shown below. It contains three attitudes which may be adjusted to focus the risk image on particular perceptions. Burden of proof quantifies the perceiver’s attitude towards the meaning of absence of evidence. Dispute tolerance probes the risk perceiver’s interpretation of differences in opinion and judgment, differences in models, and differences in information regarding the severity of an adverse event. Uncertainty display gauges the risk perceiver’s attitudes towards the importance of uncertainty in a risk assessment.
Adusting the attitude sliders changes the image of perceived risk. The figure below shows some of the ways the risk image changes when the Burden of proof settings are adjusted.