Probabilistic Sensitivity Analysis: Be a Bayesian

Probabilistic sensitivity analysis: be a Bayesian.

OBJECTIVE To give guidance in defining probability distributions for model inputs in probabilistic sensitivity analysis (PSA) from a full Bayesian perspective. METHODS A common approach to defining probability distributions for model inputs in PSA on the basis of input-related data is to use the likelihood of the data on an appropriate scale as the foundation for the distribution around the i...

Large-sample Bayesian posterior distributions for probabilistic sensitivity analysis.

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 for probabilities, rates, and relative effect parameters, for both controlled and...

A Bayesian approach to probabilistic sensitivity analysis in structured benefit-risk assessment.

Quantitative decision models such as multiple criteria decision analysis (MCDA) can be used in benefit-risk assessment to formalize trade-offs between benefits and risks, providing transparency to the assessment process. There is however no well-established method for propagating uncertainty of treatment effects data through such models to provide a sense of the variability of the benefit-risk ...

A Bayesian approach to sensitivity analysis.

Sensitivity analysis has traditionally been applied to decision models to quantify the stability of a preferred alternative to parametric variation. In the health literature, sensitivity measures have traditionally been based upon distance metrics, payoff variations, and probability measures. We advocate a new approach based on information value and argue that such an approach is better suited ...

1 2 Large - Sample Bayesian Posterior Distributions for 3 Probabilistic Sensitivity Analysis