non-parametric bayesian value of information analysis
DESCRIPTION
Non-parametric Bayesian value of information analysis. Aim: To inform the efficient allocation of research resources Objectives: To use all the available information regarding the alternative sources of funding To be sufficiently simple to apply to enable widespread adoption. Requirements. - PowerPoint PPT PresentationTRANSCRIPT
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Non-parametric Bayesian value of information analysis
Aim: •To inform the efficient allocation of research resources
Objectives:•To use all the available information regarding the alternative sources of funding•To be sufficiently simple to apply to enable widespread adoption
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Requirements
•A fully populated stochastic decision model (preferably one that facilitates analyses of 1st order uncertainty)
•A method for generating a set of hypothetical data describing the most likely outcome of any future research
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The stochastic decision model
Comparing adjuvant therapies for early breast cancer
Discrete event simulation (DES) model
4 categories of input parameters, 2 forms of probability distribution
Beta: proportions and utility values
Gamma: Survival times and costs
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VOI analysis components
•Expected value of perfect information (EVPI)
•Expected value of sample information
•(EVSI)
•Expected net benefits of sampling (ENBS)
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EVPI process
If T1 is the mean cost-effective intervention, the EVPI(episode) is the sum
of the incremental net benefits in the proportion of iterations in which T0
displays positive incremental net benefits
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EVPI(population) =
P
p
pepisode r
IEVPI
1 )1(
I: number of episodes in specified periodp: periodP: number of periods relevant to decisionR: discount rate
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EVSI definition
Difference in net benefits between the baseline EVPI and the EVPI estimated using updated probability distributions.
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EVSI assumptions
Additional data will yield the same mean values as the observed data
- if additional data is sampled from prior distribution is there a potential for EVSI decreasing with increased sample?
The additional data will reduce the variance of the baseline probability distributions
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EVSI process
Estimate the proportion of patients informing each input parameter.
Update original probability distributions using the properties of the conjugate
families of the beta and gamma distributions.
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EVSI process
Estimate the optimal sample allocation between the interventions.
Analyse the model and the EVPI.
Compare the baseline and updated EVPI.
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ENBS definition
The EVSI minus the cost of obtaining the additional data
)( 011
var TTT
iablefixedpopulation CCn
nCCEVSI
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Appropriateness of…
• Beta and Gamma distributions
• Assumption regarding values of additional data
• Neyman’s formula for sample allocation
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Further research required…
• Methods for estimation of ‘length of application of research’
• Impact of time required to obtain additional data– Estimate ENBS on basis of length of research?
• Accounting for relevant data collected in parallel trials
• Influence on the structure of the model