title: Globally optimal trial design for local decision making.
abstract: Value of information methods allows decision makers to identify efficient trial design following a principle of maximizing the expected value to decision makers of information from potential trial designs relative to their expected cost. However, in health technology assessment (HTA) the restrictive assumption has been made that, prospectively, there is only expected value of sample information from research commissioned within jurisdiction. This paper extends the framework for optimal trial design and decision making within jurisdiction to allow for optimal trial design across jurisdictions. This is illustrated in identifying an optimal trial design for decision making across the US, the UK and Australia for early versus late external cephalic version for pregnant women presenting in the breech position. The expected net gain from locally optimal trial designs of US$0.72M is shown to increase to US$1.14M with a globally optimal trial design. In general, the proposed method of globally optimal trial design improves on optimal trial design within jurisdictions by: (i) reflecting the global value of non-rival information; (ii) allowing optimal allocation of trial sample across jurisdictions; (iii) avoiding market failure associated with free-rider effects, sub-optimal spreading of fixed costs and heterogeneity of trial information with multiple trials.
authors.affiliation: Flinders Centre for Clinical Change and Health Care Research, Flinders University, Adelaide, SA, Australia. simon.eckermann@flinders.edu.au
authors.forename: Simon
authors.initials: S
authors.lastname: Eckermann
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Willan
mesh_terms: D001315:Australia; D003362:Cost-Benefit Analysis; D003431:Cross-Cultural Comparison; D003659:Decision Making, Organizational; D004059:Diffusion of Innovation; D005260:Female; D006801:Humans; D018803:Models, Economic; D015337:Multicenter Studies as Topic / Q000191:economics / Q000379:methods*; D011247:Pregnancy; D016032:Randomized Controlled Trials as Topic / Q000191:economics / Q000379:methods*; D012041:Regional Medical Programs / Q000191:economics*; D012109:Research Support as Topic / Q000191:economics*; D018401:Sample Size; D013673:Technology Assessment, Biomedical / Q000191:economics / Q000379:methods*; D006113:United Kingdom; D014481:United States; D014710:Version, Fetal / Q000191:economics