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Justice, A.C., Covinsky, K.E., and Berlin, J.A. (1999, March). "Assessing the generalizability of prognostic information." (National Research Service Award training grant T32 HS00009). Annals of Internal Medicine 130(6), pp. 515-524.

Physicians are frequently asked to assess a patient's prognosis but often worry that their assessments will prove inaccurate. Prognostic systems have been developed to enhance the accuracy of such assessments. This paper describes an approach for evaluating prognostic systems based on the accuracy and generalizability of the system's prediction. Generalizability of a prognostic system is commonly limited to a single historical period, geographic location, methodologic approach, disease spectrum, or followup interval. However, the more diverse the previous settings in which the system has been tested and found accurate, the more likely it will generalize to an untested setting, conclude these authors. They describe a working hierarchy of the cumulative generalizability of prognostic systems. Their approach is illustrated in a structured review of the Dukes and Jass staging systems for colon and rectal cancer and applied to a young man with colon cancer.

Sanders, G.D., Hagerty, C.G., Sonnenberg, F.A., and others (1999). "Distributed decision support using a Web-based interface: Prevention of sudden cardiac death." (AHCPR grant HS08362). Medical Decision Making 19, pp. 157-166.

Decision models provide an analytic framework for representing the evidence, outcomes, and preferences in a clinical decision. Authors of clinical practice guidelines increasingly depend on decision models to inform the guideline recommendations. However, the widespread use of decision models is often limited by the lack of platform-independent software that geographically dispersed users can access and use easily without extensive training. To address these limitations, the authors developed a Web-based interface for previously developed decision models. They describe the use and functionality of the interface using a decision model that evaluates the cost-effectiveness of strategies for preventing sudden cardiac death. The system allows an analyst to use a Web browser to interact with the decision model, and it also provides linkages to an explanation of the model.

Silber, J.H., Rosenbaum, P.R., Koziol, L.F., and others (1999, April). "Conditional length of stay." (AHCPR grant HS06560). Health Services Research 34(1), pp. 349-363.

Hospital stays are likely to be prolonged when there are complications. By studying conditional length of stay (CLOS), one can determine when the rate of hospital discharge begins to diminish without the need to directly observe complications, according to this study. The authors derived the CLOS measure from the statistics and engineering reliability literature and applied it using data on pediatric appendectomy and pneumonia admissions. They analyzed abstracted records from 7,777 pediatric pneumonia cases and 3,413 pediatric appendectomy cases. They found that an extended pattern of LOS by day 3 was associated with declining rates of discharge. This extended pattern coincided with increasing patient complication rates. Future validating studies will be required to determine whether variation in extended stay suggests inadequate management of complications or, conversely, thorough and complete medical care.

Zhou, X-H., Brizendine, E.J., and Pritz, M.B. (1999). "Methods for combining rates from several studies." (AHCPR grant HS08559). Statistics in Medicine 18, pp. 557-566.

Several independent groups often conduct studies to estimate a procedure's success rate. Researchers then may combine the results of these studies in the hopes of obtaining a better estimate for the true unknown success rate of the procedure. This paper presents two hierarchical methods for estimating the overall rate of success. Both methods take into account the within-study and between-study variation and assume that the number of successes within each study follows a binomial distribution given each study's own success rate. Both methods use the maximum likelihood approach to derive an estimate for the overall success rate and to construct the corresponding confidence intervals. The authors present an approach to estimating a confidence interval for the success rate when the number of studies is small and then perform a simulation study to compare the two methods.

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AHCPR Publication No. 99-0038
Current as of June 1999

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