Uncertainty assessment of input parameters for economic evaluation: Gausss error propagation, an alternative to established methods

Medical Decision Making

Volumn 30, Issue 3, 2010, Pages 304-313

  Stollenwerk, Björn ^a,b,c   Stock, Stephanie ^c   Siebert, Uwe ^b,d,e   Lauterbach, Karl W ^c,e   Holle, Rolf ^a  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b UMITUniversity for Health Sciences   (Austria)

^c UNIVERSITY OF COLOGNE   (Germany)

^d MASSACHUSETTS GENERAL HOSPITAL   (United States)

^e HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Decision analytic modeling; Error propagation law; Health economic evaluation; Uncertainty assessment.

Indexed keywords

AGED; ARTICLE; COST BENEFIT ANALYSIS; DECISION SUPPORT SYSTEM; ECONOMICS; FEMALE; GERMANY; HEALTH CARE COST; HEALTH CARE DELIVERY; HEALTH INSURANCE; HUMAN; MALE; METHODOLOGY; OUTCOME ASSESSMENT; REPRODUCIBILITY; STATISTICAL MODEL; STATISTICS; UNCERTAINTY;

AGED; COST-BENEFIT ANALYSIS; DECISION SUPPORT TECHNIQUES; DELIVERY OF HEALTH CARE; FEMALE; GERMANY; HEALTH CARE COSTS; HUMANS; INSURANCE, HEALTH; MALE; MODELS, ECONOMETRIC; OUTCOME ASSESSMENT (HEALTH CARE); REPRODUCIBILITY OF RESULTS; UNCERTAINTY;

EID: 77952973713     PISSN: 0272989X     EISSN: 1552681X     Source Type: Journal    
DOI: 10.1177/0272989X09347015     Document Type: Article

References (23)

1. Doubilet P., Begg CB, Weinstein MC, Braun P., McNeil BJ Probabilistic sensitivity analysis using Monte Carlo simulation: a practical approach. Med Decis Making. 1985 ; 5: 157-177.
2. Briggs A., Claxton K., Sculpher M. Decision Modelling for Health Economic Evaluation. Oxford, UK: Oxford University Press; 2006.
3. Briggs AH, Wonderling DE, Mooney CZ Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation. Health Econ. 1997 ; 6: 327-340.
4. Claxton K., Sculpher M., McCabe C., et al. Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Econ. 2005 ; 14: 339-347.
5. Teerawattananon Y., Mugford M., Tangcharoensathien V. Economic evaluation of palliative management versus peritoneal dialysis and hemodialysis for end-stage renal disease: evidence for coverage decisions in Thailand. Value Health. 2007 ; 10: 61-72.
6. Efron B., Tibshirani RJ An Introduction to the Bootstrap. Vol. 57. New York: Chapman & Hall ; 1993.
7. Cecchi GC Error analysis of the parameters of a least-squares determined curve when both variables have uncertainties. Meas Sci Technol. 1991 ; 2: 1127-1128.
8. Hall BD On the propagation of uncertainty in complex-valued quantities. Metrologia. 2004 ; 41: 173-177.
9. Hartung J., Elpelt B., Klösener K-H. Statistik: Lehr- und Handbuch der angewandten Statistik. München: Oldenbourg ; 2002.
10. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2008.
11. Wood SN Generalized Additive Models: An Introduction with R. London: Chapman & Hall/CRC ; 2006.
12. Lauterbach KW, Wille E., Lüngen M., et al. Modell eines fairen Wettbewerbs durch den Risikostrukturausgleich. Mannheim: Institut für Gesundheitsökonomie und klinische Epidemiologie (IGKE) der Universität zu Köln und Lehrstuhl für Volkswirtschaftslehre der Universität Mannheim, Köln ; 2001.
13. Stock S., Redaelli M., Luengen M., et al. Asthma: prevalence and cost of illness. Eur Respir J. 2005 ; 25: 47-53.
14. Stock SA, Stollenwerk B., Redaelli M., Civello D., Lauterbach KW Sex differences in treatment patterns of six chronic diseases: an analysis from the German statutory health insurance. J Womens Health. 2008 ; 17: 343-354.
15. Wood SN Thin plate regression splines. J R Stat Soc B. 2003 ; 65: 95-114.
16. Frick KD, Sorensen SV, Hollenbeak C. How inducing covariation in simulated HDL-C, triglyceride, and total cholesterol data affects Framingham risk equation results. Value Health. 2008 ; 11: 939-945.
17. Gentle JE Elements of Computational Statistics. New York: Springer ; 2002.
18. Pasta DJ, Taylor JL, Henning JM Probabilistic sensitivity analysis incorporating the bootstrap: an example comparing treatments for the eradication of Helicobacter pylori. Med Decis Making. 1999 ; 19: 353-363.
19. Windmeijer F., Kontodimas S., Knapp M., Brown J., Haro JM Methodological approach for assessing the cost-effectiveness of treatments using longitudinal observational data: the SOHO study. Int J Technol Assess Health Care. 2006 ; 22: 460-468.
20. OHagan A., Stevenson M., Madan J. Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA. Health Econ. 2007 ; 16: 1009-1023.
21. Stevenson MD, Oakley J., Chilcott JB Gaussian process modeling in conjunction with individual patient simulation modeling: a case study describing the calculation of cost-effectiveness ratios for the treatment of established osteoporosis. Med Decis Making. 2004 ; 24: 89-100.
22. Haylock R., OHagan A. On inference for outputs of computationally expensive computer models with uncertainty on the inputs. In: Bernado JM, Berger JO, Dawid AP, Smith AFM, eds. Bayesian Statistics 5. Oxford, UK: Oxford University Press ; 1996. p 629-637.
23. Shao J., Tu D. The Jackknife and Bootstrap. New York: Springer ; 1995.