Sample RapidAnalysis Exercises 
Yokabid Worku examines human deaths caused by exposure to Anthrax

Kwaku Boateng analyzes seniors who move into an assisted living program

Vikas Arya accessed breast cancer risks

I. J. also predicted breast cancer risk by using casual graphs to establish conditional independence

S. R. examines who among patients with HIV will develop AIDS

J. Y. predicted which nurses would leave bedside patient care

Kalpana Satyal analyzed patient falls

Learning Tools 
Listen to a narrated presentation on modeling uncertainty

Listen to a narrated presentation on checking conditional independence through causal graphs

Access an online tool for generating cases based on clues and clue levels

Watch an animated example on calculating correlations with Excel’s data analysis tool pack

Watch an animated example on creating an xy scatter chart and calculating correlations in Excel

Download slides on modeling uncertainty

Websites of Interest 
The Merck Manual’s section on how to revise test probabilities using Bayes’s theorem

List of articles on the application of Bayesian probability models to realistic settings

Additional Readings 
Armstrong, K., A. Eisen, and B. Weber. 2000. “Primary Care: Assessing the Risk of Breast Cancer.” New England Journal of Medicine 342 (8): 564–71. This article discusses how various clues can be used to predict breast cancer risk. Average risk, epidemiologic risk factors, and riskprediction factors are also discussed.

Deeks, J. J. 2001. “Systematic Reviews of Evaluations of Diagnostic and Screening Tests.” BMJ 323 (7305):157–62. This article reviews several studies of diagnostic accuracy with respect to the quality of the study and the empirical evidence produced. Sources of heterogeneity, pooling sensitivities and specificities, pooling likelihood ratios, diagnostic odds ratios, and operating characteristic curves are also discussed. A manager can use this information to assess practice patterns of clinicians.

McFall, R. M., and T. A. Treat. 1999. “Quantifying the Information Value of Clinical Assessments with Signal Detection Theory.” Annual Review of Psychology 50:215–41. This article examines the utility of signal detection theory (SDT) to providing a ubiquitous measure to express actual value of assessment data, irrespective of “cutting points, base rates, or a particular application.” This is a response to the fact that current methods used in determining the accuracy of

Armstrong, K., A. Eisen, and B. Weber. 2000. “Primary Care: Assessing the Risk of Breast Cancer.” New England Journal of Medicine 342 (8): 564–71. This article discusses how various clues can be used to predict breast cancer risk. Average risk, epidemiologic risk factors, and riskprediction factors are also discussed.

Deeks, J. J. 2001. “Systematic Reviews of Evaluations of Diagnostic and Screening Tests.” BMJ 323 (7305):157–62. This article reviews several studies of diagnostic accuracy with respect to the quality of the study and the empirical evidence produced. Sources of heterogeneity, pooling sensitivities and specificities, pooling likelihood ratios, diagnostic odds ratios, and operating characteristic curves are also discussed. A manager can use this information to assess practice patterns of clinicians.

McFall, R. M., and T. A. Treat. 1999. “Quantifying the Information Value of Clinical Assessments with Signal Detection Theory.” Annual Review of Psychology 50:215–41. This article examines the utility of signal detection theory (SDT) to providing a ubiquitous measure to express actual value of assessment data, irrespective of “cutting points, base rates, or a particular application.” This is a response to the fact that current methods used in determining the accuracy of assessment data are complicated by selection of cutting points, base rate of events and assessment goals.

Neiner, J. A., E. H. Howze, and M. L. Greaney. 2004. “Using Scenario Planning in Public Health: Anticipating Alternative Futures.” Health Promotion Practice 5 (1): 69–79.

Schwingl, P. J., H. W. Ory, and C. M. Visness. 1999. “Estimates of the Risk of Cardiovascular Death Attributable to LowDose Oral Contraceptives in the United States.” American Journal of Obstetrics and Gynecology 180 (1): 241–9. This article attempts to evaluate the risk of mortality from breast disease due to lowdose contraceptives. The example was classified into smoking and nonsmoking.

Weber, E. U. 1994. “From Subjective Probabilities to Decision Weights: The Effect of Asymmetric Loss Functions on the Evaluation of Uncertain Outcomes and Events.” Psychological Bulletin 115 (2): 228–42. This article discusses alternatives to expected utility theory in making decisions in the real world. It describes how people make decisions as opposed to how they should.
