The german coronary artery disease risk screening model: Development, validation, and application of a decision-analytic model for coronary artery disease prevention with statins.

Coronary artery disease (CAD) is a major cause of death in industrial countries, leading to high health-related costs and decreased quality of life. OBJECTIVE: To develop and validate a decision-analytic model for CAD risk screening in Germany (German Coronary Artery Disease Screening Model). DESIGN: . Markov model. Target POPULATION: Age- and gender-specific cohorts of the German population. Data Sources. Mortality rates posted by the German Federal Statistical Office, the German Health Survey, social health insurance institutions, the MONICA Augsburg study, and the literature. Time Horizon. Lifetime. Interventions. CAD risk screening for high-risk individuals using Framingham risk equation and use of statins as the primary preventive measure, compared with a setting without screening. Outcome MEASURES: Life-years (LY) gained, quality-adjusted life-years (QALYs) gained. RESULTS: The model-based CAD incidence corresponds well with empirical data from the MONICA Augsburg study. Health outcomes depend on the screening threshold (cutoff value of Framingham 10-year risk) and on the age and gender of the cohort screened (0.03 to 0.26 LYs and 0.06 to 0.42 QALYs gained per person screened in cohorts of 50- and 60-year-old men and women, respectively). CONCLUSIONS: The model provides a valid tool for evaluating the long-term effectiveness of CAD risk screening in Germany. Using statins as a primary prevention intervention for CAD in high-risk individuals identified by screening could improve the long-term health of the German population

Uncertainty assessment of input parameters for economic evaluation: Gauss's error propagation, an alternative to established methods.

In decision modeling for health economic evaluation, bootstrapping and the Cholesky decomposition method are frequently used to assess parameter uncertainty and to support probabilistic sensitivity analysis. An alternative, Gauss's error propagation law, is rarely known but may be useful in some settings. Bootstrapping, the Cholesky decomposition method, and the error propagation law were compared regarding standard deviation estimates of a hypothetic parameter, which was derived from a regression model fitted to simulated data. Furthermore, to demonstrate its value, the error propagation law was applied to German administrative claims data. All 3 methods yielded almost identical estimates of the standard deviation of the target parameter. The error propagation law was much faster than the other 2 alternatives. Furthermore, it succeeded the claims data example, a case in which the established methods failed. In conclusion, the error propagation law is a useful extension of parameter uncertainty assessment

Modellstatische und analytische Untersuchungen an einer zweistegigen, quertraegerlosen Plattenbalkenbruecke im Zuge der Ortsumgehung Osterode/Harz sowie Kontrolle der Ergebnisse durch in-situ-Messungen Band 1. Band 2: Anhang

TIB: RN 4195 (7416,1+2) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman