Probabilistic Sensitivity Analysis: Be a Bayesian

ABSTRACT 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 us...

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Bibliographic Details
Published in:Value in health 2009-11, Vol.12 (8), p.1210-1214
Main Authors: Boshuizen, Hendriek C., PhD, van Baal, Pieter H.M., PhD
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian methods
Confidence Intervals
Data Interpretation, Statistical
Guidance
Humans
Internal Medicine
Likelihood Functions
Logistic Models
maximum likelihood estimation
Models, Economic
prior probability distribution
probabilistic sensitivity analysis
Relative risks
Risk
Sensitivity analysis
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Summary:ABSTRACT 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 inputs. We will look at this approach from a Bayesian perspective, derive the implicit prior distributions in two examples (proportions and relative risks), and compare these to alternative prior distributions. Results In cases where data are sparse (in which case sensitivity analysis is crucial), commonly used approaches can lead to unexpected results. Weshow that this is because of the prior distributions that are implicitly assumed, namely that these are not as “uninformative” or “vague” as believed. We propose priors that we believe are more sensible for two examples and which are just as easy to apply. Conclusions Input probability distributions should not be based on the likelihood of the data, but on the Bayesian posterior distribution calculated from this likelihood and an explicitly stated prior distribution.
ISSN:1098-3015
1524-4733
DOI:10.1111/j.1524-4733.2009.00590.x