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Evaluating risk profiles for pharmaceutical side effects

 

Software: RAMAS Risk Imaging

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Funding: Pfizer, Inc.

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Authors: David Slavin, W. Troy Tucker, and Scott Ferson

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We developed an approach (RAMAS Risk Imaging) to communicate uncertainty about risk by decomposing risk into two basic elements: first, the frequency of each kind of harm associated with a hazard, and second, the adversity of each of those harms. This method incorporates sampling error, measurement error, and bias in uncertainty about the adversity o fa harm, while differences in opinion, measurement error, and choice of dimensions contribute to uncertainty about adversity. We image risk profiles as an area defined by these uncertainties. Alternate risk visualizations can be contrasted across management choices or different perceptions of risk to facilitate communication of risks and aid in decision making. In developing this approach, we demonstrated possible risk profiles of three common hypertension drugs.

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Spatial clustering of childhood cancers in the Denver Metroplex

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Software: RAMAS/GIS

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Funding: Radian Corporation, with funding from Electric Power Research Institute (EPRI)

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Authors: Scott Ferson

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Location of study: Denver

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Although we could detect no spatial clustering of childhood cancers at the finest resolution of individual cases and controls, analysis with aggregated data using census information detected statistically significant spatial clustering. The intensity (and significance) of the spatial clustering was even stronger at the level of entire cities in the Denver metroplex. The principal finding is that, when the locations of childhood cancers are aggregated into area/frequency data, statistical tests reveal significant spatial clustering.

 

This conclusion is robust in the sense that it is independent of many details of the analysis and the data and seems to persist over spatial resolutions ranging from the scale of a city to that of a census tract. This confirms the finding that there is strong spatial inhomogeneity in childhood cancer incidence across the region. The inhomogeneity cannot be removed simply by accounting for changes in the population density of children. This observation of clustering of childhood cancers has several important ramifications for the study of this cancer's phenomenology. The findings call into question many of the other statistical procedures already conducted on this data set that have assumed spatial homogeneity of disease etiology or incidence. Although often unstated, such an assumption is very common and probably affects many if not the majority of the most prominent conclusions that have been made about the Denver cancer data.

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Detecting Disease Clusters in Structured Environments

 

Software: RAMAS/GIS​

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Funding: National Institutes of Health (NIH) and by New York State Science and Technology Foundation

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Presented/published at: Ferson, S. 1996. Reliable calculation in probabilistic logic: accounting for small sample size and model uncertainty. Proceedings of Intelligent Systems: A Semiotic Perspective. National Institute for Standards and Technology, Gaithersburg, Maryland [to appear].

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Authors: Scott Ferson

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One of the difficulties faced by health professionals in detecting of disease clusters is that the data sets are often small and inferences must be based on a relative handful of observations. It is crucial for the health professional to know what statistical tests are best in these small-environment problems, and to have these methods available in a user-friendly computer package. A variety of new, rapid, and exact combinatorial expressions for cluster analysis of patterns of disease have been proposed. Investigations into the statistical power of both these and other previously published methods for cluster detection in structured small environments will be used to recommend different tests for different kinds of problems and different amounts of data.

 

An interactive program called EPIC (Exact Probabilities for Incidence Clustering) that includes an intuitive interface and a thorough set of tutorials and guidelines will help the professional choose the best statistical test for a particular problem. EPIC will allow the health professional to investigate allegations of disease clustering within small, structured environments, such as families, sibships, wards, classrooms, cell blocks, job types, age classes or locations within a building. When sample sizes are small (as is almost always the case in real circumstances of public health concern), exact statistical methods are necessary since the approximate methods usually used only yield accurate estimates with data sets are large.

 

Exact methods guarantee that Type I error can be controlled to any desired level. Although a few exact methods based on matrix occupancy models have previously been described for data sets with perfectly regular structure, no exact methods were applicable when, for instance, families were of different sizes. EPIC will provide, for the first time, general exact statistical methods for use with small data sets in structured environments. It will allow public health professionals in the research and regulatory communities access to these new methods in a flexible and powerful microcomputer implementation.

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RAMAS® software has been applied to evaluating quality assurance methods for Monte Carlo risk analysis, and stochastic cell proliferation models of carcinogenesis. 

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