Globally optimal trial design for local decision making.

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.

[1]  K M Thompson,et al.  A dynamic programming approach to the efficient design of clinical trials. , 2001, Journal of health economics.

[2]  Andrew Briggs,et al.  Statistical Analysis of Cost-Effectiveness Data , 2006 .

[3]  J Mullahy.  What you don't know can't hurt you? Statistical issues and standards for medical technology evaluation. , 1996, Medical care.

[4]  John Gittins,et al.  Quantitative Methods in the Planning of Pharmaceutical Research , 1996 .

[5]  A Briggs,et al.  Confidence intervals or surfaces? Uncertainty on the cost-effectiveness plane. , 1998, Health economics.

[6]  Andrew R Willan,et al.  The Option Value of Delay in Health Technology Assessment , 2008, Medical decision making : an international journal of the Society for Medical Decision Making.

[7]  The assessment of the economic return from controlled clinical trials , 2003, The European Journal of Health Economics.

[8]  D. Heitjan,et al.  Fieller's method and net health benefits. , 2000, Health economics.

[9]  N. Zethraeus,et al.  Definition, interpretation and calculation of cost-effectiveness acceptability curves. , 2000, Health economics.

[10]  Darren McKay,et al.  External cephalic version beginning at 34 weeks' gestation versus 37 weeks' gestation: a randomized multicenter trial. , 2003, American journal of obstetrics and gynecology.

[11]  A. Briggs,et al.  Statistical Analysis of Cost-effectiveness Data: Willan/Statistical Analysis of Cost-effectiveness Data , 2006 .

[12]  Hamid Pezeshk,et al.  Bayesian techniques for sample size determination in clinical trials: a short review , 2003, Statistical methods in medical research.

[13]  Hamid. Pezeshk,et al.  How large should a clinical trial be , 2000 .

[14]  J Halpern,et al.  Designing a cost-effective clinical trial. , 1995, Statistics in Medicine.

[15]  K Claxton,et al.  An economic approach to clinical trial design and research priority-setting. , 1996, Health economics.

[16]  S. Stearns,et al.  Estimating confidence intervals for cost-effectiveness ratios: an example from a randomized trial. , 1996, Statistics in medicine.

[17]  C. J. Adcock,et al.  The choice of sample size. Commentaries. Author's reply , 1997 .

[18]  M Johannesson,et al.  A Note on Confidence Intervals in Cost-Effectiveness Analysis , 1998, International Journal of Technology Assessment in Health Care.

[19]  R J Cook,et al.  Using inverse-weighting in cost-effectiveness analysis with censored data , 2002, Statistical methods in medical research.

[20]  John Gittins,et al.  A Behavioral Bayes Method for Determining the Size of a Clinical Trial , 2000 .

[21]  Howard Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[22]  Andrew R Willan,et al.  Time and expected value of sample information wait for no patient. , 2008, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[23]  P. Wakker,et al.  Confidence intervals for cost/effectiveness ratios. , 1995, Health economics.

[24]  D Y Lin,et al.  Incremental net benefit in randomized clinical trials , 2001, Statistics in medicine.

[25]  J Hornberger,et al.  The cost-benefit of a randomized trial to a health care organization. , 1998, Controlled clinical trials.

[26]  Hamid Pezeshk,et al.  A Fully Bayesian Approach to Calculating Sample Sizes for Clinical Trials with Binary Responses , 2002 .

[27]  R. Baltussen,et al.  The interpretation of results of economic evaluation: explicating the value of health. , 1997, Health economics.

[28]  M J Al,et al.  Costs, effects and C/E-ratios alongside a clinical trial. , 1994, Health economics.

[29]  Andrew R Willan,et al.  The value of information and optimal clinical trial design , 2005, Statistics in medicine.

[30]  Andrew Briggs,et al.  Health Technology Assessment in the Cost-Disutility Plane , 2008, Medical decision making : an international journal of the Society for Medical Decision Making.

[31]  Andrew R Willan,et al.  Incremental net benefit in randomized clinical trials with quality‐adjusted survival , 2003, Statistics in medicine.

[32]  D Polsky,et al.  Confidence intervals for cost-effectiveness ratios: a comparison of four methods. , 1997, Health economics.

[33]  A R Willan,et al.  Analysis, sample size, and power for estimating incremental net health benefit from clinical trial data. , 2001, Controlled clinical trials.

[34]  H. Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[35]  Andrew R Willan,et al.  Expected value of information and decision making in HTA. , 2007, Health economics.

[36]  K Claxton,et al.  The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. , 1999, Journal of health economics.

[37]  Hamid Pezeshk,et al.  Sample Size Determination in Clinical Trials , 1999 .

[38]  Bernie J. O'Brien,et al.  In Search of Power and Significance: Issues in the Design and Analysis of Stochastic Cost-Effectiveness Studies in Health Care , 1994, Medical care.

[39]  A R Willan,et al.  Confidence intervals for cost-effectiveness ratios: an application of Fieller's theorem. , 1996, Health economics.

[40]  A O'Hagan,et al.  Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness , 2001, Medical decision making : an international journal of the Society for Medical Decision Making.

[41]  M. J. R. Healy,et al.  Economic Choice of the Amount of Experimentation , 1956 .

[42]  J. Mullahy,et al.  Valuing health care: Statistical issues in cost–effectiveness analyses , 1995 .

[43]  A H Briggs,et al.  A Bayesian approach to stochastic cost-effectiveness analysis. , 1999, Health economics.

[44]  John Mullahy,et al.  Net Health Benefits: A New Framework for the Analysis of Uncertainty in Cost-Effectiveness Analysis , 1998 .

[45]  D. Lindley.  The choice of sample size , 1997 .

[46]  B W Brown,et al.  The sample size for a clinical trial: A Bayesian–decision theoretic approach , 2001, Statistics in medicine.

[47]  A H Briggs,et al.  Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation. , 1997, Health economics.

[48]  C E Phelps,et al.  On the (Near) Equivalence of Cost-Effectiveness and Cost-Benefit Analyses , 1991, International Journal of Technology Assessment in Health Care.