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Info and Chapters
Decision Analysis for Healthcare Managers
Farrokh Alemi, PhD
David H. Gustafson, PhD

Chapter 4: Modeling Uncertainty
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Companion Items

Sample Rapid-Analysis 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 x-y 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 risk-prediction 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 risk-prediction 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 Low-Dose 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 low-dose 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.
 
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