Some methodological issues in value of information analysis: an application of partial EVPI and EVSI to an economic model of Zanamivir Karl Claxton and.

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Some methodological issues in value of information analysis: an application of partial EVPI and EVSI to an economic model of Zanamivir Karl Claxton and Tony Ades

Partial EVPIs Light at the end of the tunnel…… ……..maybe it’s a train

A simple model of Zanamivir

.000.007.013.020.026 (£40.00)(£20.00)£0.00£20.00£40.00 Normal Distribution Mean = (£0.51) Std Dev = £12.52 inb Distribution of inb

EVPI for the decision EVPI = EV(perfect information) - EV(current information)

Partial EVPI EVPI pip = EV(perfect information about pip) - EV(current information) EV(optimal decision for a particular resolution of pip) particular resolution of pip) Expectation of this difference over all resolutions of pip EV(prior decision for the same resolution of pip) same resolution of pip) -

Partial EVPI Some implications:  information about an input is only valuable if it changes our decision  information is only valuable if pip does not resolve at its expected value General solution:  linear and non linear models  inputs can be (spuriously) correlated

Felli and Hazen (98) “short cut” EVPI pip = EVPI when resolve all other inputs at their expected value Appears counter intuitive:  we resolve all other uncertainties then ask what is the value of pip ie “residual” EVPIpip ? But:  resolving at EV does not give us any information Correct if:  linear relationship between inputs and net benefit  inputs are not correlated

So why different values?  The model is linear  The inputs are independent?

“Residual” EVPI  wrong current information position for partial EVPI  what is the value of resolving pip when we already have perfect information about all other inputs?  Expect residual EVPI pip < partial EVPI pip EVPI when resolve all other inputs at each realisation ?

Thompson and Evans (96) and Thompson and Graham (96) inb simplifies to: inb = Rearrange: pip: inb = pcz: inb = phz: inb = rsd: inb = upd: inb = phs: inb = pcs: inb =  Felli and Hazen (98) used a similar approach  Thompson and Evans (96) is a linear model  emphasis on EVPI when set others to joint expected value  requires payoffs as a function of the input of interest

Reduction in cost of uncertainty  intuitive appeal  consistent with conditional probabilistic analysis RCU E(pip) = EVPI - EVPI(pip resolved at expected value) But  pip may not resolve at E(pip) and prior decisions may change  value of perfect information if forced to stick to the prior decision ie the value of a reduction in variance  Expect RCU E(pip) < partial EVPI

Reduction in cost of uncertainty spurious correlation again? RCU pip = E pip [EVPI – EVPI(given realisation of pip)] = partial EVPI RCU pip = EVPI – E pip [EVPI(given realisation of pip)] = [EV(perfect information) - EV(current information)] - E pip [EV(perfect information, pip resolved) - EV(current information, pip resolved)] E pip [EV(perfect information, pip resolved) - EV(current information, pip resolved)]

EVPI for strategies Value of including a strategy?  EVPI with and without the strategy included  demonstrates bias  difference = EVPI associated with the strategy?  EV(perfect information, all included) – EV(perfect information, excluded) E all inputs [Max d (NB d | all inputs )] – E all inputs [Max d-1 (NB d-1 | all inputs )]

Conclusions on partials Life is beautiful …… Hegel was right ……progress is a dialectic Maths don’t lie …… ……but brute force empiricism can mislead

EVSI…… …… it may well be a train Hegel’s right again! ……contradiction follows synthesis

EVSI for model inputs  generate a predictive distribution for sample of n  sample from the predictive and prior distributions to form a preposterior  propagate the preposterior through the model  value of information for sample of n  find n* that maximises EVSI-cost sampling

EVSI for pip Epidemiological study n  prior:pip  Beta ( ,  )  predicitive:rip  Bin(pip, n)  preposterior:pip’ = (pip(  +  )+rip)/((  +  +n)  as n increases var(rip*n) falls towards var(pip)  var(pip’) < var(pip) and falls with n  pip’ are the possible posterior means

EVSIpip = reduction in the cost of uncertainty due to n obs on pip = difference in partials (EVPIpip – EVPIpip’) E pip [E other [Max d (NB d | other, pip )] - Max d E other (NB d | other, pip )] - E pip’ [E other [Max d (NB d | other, pip’ )] - Max d E other (NB d | other, pip’ )] pip’has smaller var so any realisation is less likely to change decision E pip [E other [Max d (NB d | other, pip )] > E pip’ [E other [Max d (NB d | other, pip’ )] E(pip’) = E(pip) E pip [Max d E other (NB d | other, pip )] = E pip’ [Max d E other (NB d | other, pip’ )]

EVSIpip Why not the difference in prior and preposterior EVPI?  effect of pip’ only through var(NB)  change decision for the realisation of pip’ once study is completed  difference in prior and preposterior EVPI will underestimate EVSIpip

Implications  EVSI for any input that is conjugate  generate preposterior for log odds ratio for complication and hospitalisation etc  trial design for individual endpoint (rsd)  trial designs with a number of endpoints (pcz, phz, upd, rsd)  n for an endpoint will be uncertain (n_pcz = n*pip, etc)  consider optimal n and allocation (search for n*)  combine different designs eg:  obs study (pip) and trial (upd, rsd) or obs study (pip, upd), trial (rsd)…. etc

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