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.
Stollenwerk
Björn
B
Institute of Health Economics and Clinical Epidemiology of the University of Cologne, Gleueler Strasse Cologne, Germany. bjoern.stollenwerk@helmholtz-muenchen.de
Stock
Stephanie
S
Siebert
Uwe
U
Lauterbach
Karl W
KW
Holle
Rolf
R
D000368:Aged; D003362:Cost-Benefit Analysis / Q000379:methods*; D003661:Decision Support Techniques; D003695:Delivery of Health Care / Q000191:economics*; D005260:Female; D005858:Germany; D017048:Health Care Costs / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D007348:Insurance, Health / Q000191:economics; D008297:Male; D017059:Models, Econometric; D017063:Outcome Assessment, Health Care / Q000191:economics*; D015203:Reproducibility of Results; D035501:Uncertainty