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Non-parametric Bayesian value of information analysis

This non-parametric Bayesian value of information analysis aims to inform the efficient allocation of research resources by utilizing all available information regarding alternative sources of funding. The method should be simple enough to enable widespread adoption.

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Non-parametric Bayesian value of information analysis

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  1. 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

  2. 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

  3. 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

  4. VOI analysis components • Expected value of perfect information (EVPI) • Expected value of sample information • (EVSI) • Expected net benefits of sampling (ENBS)

  5. 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

  6. EVPI(population) = I: number of episodes in specified period p: period P: number of periods relevant to decision R: discount rate

  7. EVSI definition Difference in net benefits between the baseline EVPI and the EVPI estimated using updated probability distributions.

  8. 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

  9. 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.

  10. EVSI process Estimate the optimal sample allocation between the interventions. Analyse the model and the EVPI. Compare the baseline and updated EVPI.

  11. ENBS definition The EVSI minus the cost of obtaining the additional data

  12. Appropriateness of… • Beta and Gamma distributions • Assumption regarding values of additional data • Neyman’s formula for sample allocation

  13. 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

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