1. Valuing Trial Designs from a Pharmaceutical Perspective using Value Based Pricing (VBP) Penny Watson 1, Alan Brennan 1 1 Health Economics and Decision Science, ScHARR, University of Sheffield, UK., Expected Net Benefit of Sample information (ENBS) can be useful in deciding which data collection strategy is optimal 1. The traditional ENBS is not compatible with drug development in the pharmaceutical industry because, o Traditional ENBS value trials according to the expected benefits to society, o Traditional ENBS assumes the price of the intervention is fixed. We aimed to evaluate trial designs for Systemic Lupus Erythematosus (SLE) drugs. Expected Net Benefit of Sample information (ENBS) can be useful in deciding which data collection strategy is optimal 1. The traditional ENBS is not compatible with drug development in the pharmaceutical industry because, o Traditional ENBS value trials according to the expected benefits to society, o Traditional ENBS assumes the price of the intervention is fixed. We aimed to evaluate trial designs for Systemic Lupus Erythematosus (SLE) drugs. Introduction We have illustrated how ENBS can be adapted to value clinical trials in the pharmaceutical industry using expected VBP to integrate price uncertainty into the decision criteria. Our analyses indicated that larger sample sizes are more efficient than longer trials in SLE. This very simple example took 5 days to generate 10,000 sets of trial results. The analyses can be very time-consuming to run for complex economic models. We have illustrated how ENBS can be adapted to value clinical trials in the pharmaceutical industry using expected VBP to integrate price uncertainty into the decision criteria. Our analyses indicated that larger sample sizes are more efficient than longer trials in SLE. This very simple example took 5 days to generate 10,000 sets of trial results. The analyses can be very time-consuming to run for complex economic models. Conclusion Contact: Penny Watson Postal address: ScHARR, Regents Court, 30 Regent Street, Sheffield S1 4DA, United Kingdom. Email: p.r.watson@shef.ac.ukWebsite: www.shef.ac.uk/heds Contact: Penny Watson Postal address: ScHARR, Regents Court, 30 Regent Street, Sheffield S1 4DA, United Kingdom. Email: p.r.watson@shef.ac.ukWebsite: www.shef.ac.uk/heds Contact We developed a simple CE model for SLE in which costs and QALYs were estimated analytically conditional on average lifetime disease activity, average lifetime organ damage and mortality. We sampled 10,000 trial datasets (X) for nine trial designs (n=100, n=500, n=1500, d=1, d=2, d=3) in which disease activity, organ damage and treatment withdrawal were collected. We updated the CE model with trial data using the Brennan and Karroubi Bayesian Approximation method 2-3. VBP described the maximum price given the willingness to pay threshold of the reimbursement authority (£30,000). VBP was zero if the trial endpoint was not statistically significant, or if the VBP was less than the minimum acceptable price of £1000 per year. Profit forecast (PF)was estimated from, =CE model parameters, X=data, t=drug maintenance, k=incidence, h=drug life horizon, s=market share We developed a simple CE model for SLE in which costs and QALYs were estimated analytically conditional on average lifetime disease activity, average lifetime organ damage and mortality. We sampled 10,000 trial datasets (X) for nine trial designs (n=100, n=500, n=1500, d=1, d=2, d=3) in which disease activity, organ damage and treatment withdrawal were collected. We updated the CE model with trial data using the Brennan and Karroubi Bayesian Approximation method 2-3. VBP described the maximum price given the willingness to pay threshold of the reimbursement authority (£30,000). VBP was zero if the trial endpoint was not statistically significant, or if the VBP was less than the minimum acceptable price of £1000 per year. Profit forecast (PF)was estimated from, =CE model parameters, X=data, t=drug maintenance, k=incidence, h=drug life horizon, s=market share Methods Increasing sample size and duration of follow-up increases the expected VBP. However, increasing sample size with duration of follow-up of 3 years has no impact on the Expected Value Based Price (Table i). The trial design with n = 1500 and d=1 years has the highest Expected Net Benefit (Figure i). Trials with larger sample size have greater Net Benefit. The duration of follow-up is negatively associated with Net Benefit due to the increased costs of the trials, and a shorter life-horizon of the drug. Increasing sample size and duration of follow-up increases the expected VBP. However, increasing sample size with duration of follow-up of 3 years has no impact on the Expected Value Based Price (Table i). The trial design with n = 1500 and d=1 years has the highest Expected Net Benefit (Figure i). Trials with larger sample size have greater Net Benefit. The duration of follow-up is negatively associated with Net Benefit due to the increased costs of the trials, and a shorter life-horizon of the drug. Results 1. Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. Journal of Health Economics 1999; 18(3):341-364. 2. Brennan A, Kharroubi SA. Efficient computation of partial expected value of sample information using Bayesian approximation. Journal of Health Economics 2007; 26(1):122-14 3. Brennan A, Kharroubi SA. Expected value of sample information for Weibull survival data. Health Economics 2007; 16(11):1205-1225. 1. Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. Journal of Health Economics 1999; 18(3):341-364. 2. Brennan A, Kharroubi SA. Efficient computation of partial expected value of sample information using Bayesian approximation. Journal of Health Economics 2007; 26(1):122-14 3. Brennan A, Kharroubi SA. Expected value of sample information for Weibull survival data. Health Economics 2007; 16(11):1205-1225. References Table i: Expected VBP, Profit Forecast, and Trial Costs n Follow-up 1 year2 years3 years VBP (£) ProfitCost VBP (£) ProfitCost VBP (£) ProfitCost (million £) (million (£) (million £) 100£786£461£1.1£865£458£1.15£892£420£1.2 500£902£530£1.5£905£478£1.75£905£425£2.0 1500£911£535£2.5£911£481£3.25£903£427£4.0 Figure i: