Dec 1, 2006 · We work out the details of this general proposal for a two-parameter cure-rate model, used in survival analysis. We apply our results to conduct both traditional and probabilistic sensitivity analyses for a recently published decision analysis of tamoxifen use for the prevention of breast cancer.
When a decision analyst desires a sensitivity analysis on model parameters that are estimated from data, a natural approach is to vary each parameter within one or two standard errors of its estimate. This approach can be problematic if parameter estimates are correlated or if model structure does not permit ...
... analysis of tamoxifen use for the prevention of breast cancer. Suggested Citation. Gordon B. Hazen & Min Huang, 2006. "Parametric Sensitivity Analysis Using Large-Sample Approximate Bayesian Posterior Distributions," Decision Analysis, INFORMS, vol. 3(4), pages 208-219, December. Handle: RePEc:inm:ordeca:v:3:y:2006 ...
Dive into the research topics of 'Parametric Sensitivity Analysis Using Large-Sample Approximate Bayesian Posterior Distributions'. Together they form a unique ...
In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions ...
Missing: Parametric | Show results with:Parametric
In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions ...
Missing: Parametric | Show results with:Parametric
[PDF] Large-Sample Bayesian Posterior Distributions for ...
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Bayesian methods for constructing large-sample approximate posterior distributions for probabilities, rates, and relative effect parameters, for both ...
In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions ...
Missing: Parametric | Show results with:Parametric
People also ask
What is sensitivity analysis in Bayesian approach?
Robust Bayesian analysis, also called Bayesian sensitivity analysis, investigates the robustness of answers from a Bayesian analysis to uncertainty about the precise details of the analysis. An answer is robust if it does not depend sensitively on the assumptions and calculation inputs on which it is based.
What is parameter sensitivity analysis?
Parameter sensitivity analysis is an essential method for examining mathematical models of a real-life problem. A detailed parameter sensitivity analysis gives a broad set of predictions that show how changes in a model parameter affect relevant model outputs.
The sensitivity analysis revealed that the effect of priors on parameter inferences is different from their effect on marginal density and predictive posterior distribution. In this paper, we explore the effect of the prior (or posterior) distribution on the corresponding posterior predictive distribution.
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Downloadable! In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation ...