Stephen T. Parente, Ph.D. Carlson School of Management, Department of Finance =

1. University of MinnesotaMedical Technology Evaluation and Market Research Course: MILI/PUBH 6589Spring Semester, 2013 Stephen T. Parente, Ph.D. Carlson School of Management, Department of Finance =

2. Lecture Overview • Cost / Benefit Review • Discounting • Uncertainty & Risk • Monte Carlo Analysis • SQL Server

3. Review of Cost/Benefit Analysis • CE Ratio = Change in Costs / Change in QALYs. • To calculate changes in costs measure the differences in accumulated costs associated with treatment • To calculate QALYs—compare patient outcomes. • Importance of Time and Dynamics

4. Discounting • The impact of most medical technologies stretches over time. • The future is “worth less” than the present. • The key question is how much less?

5. Whose discount rate? • Individuals? • Corporations? • Governments? • Social Planner?

6. How much does it matter? • Sometimes it is important, sometimes not. Yes for disease screenings and many preventions, no for influenza vaccine. • Important if there are long time frames and if the intervention is marginally CE. • Suppose an intervention increases HRQL by .2 for 10 years. Assume QALYs = $100,000. • Annual discount rates and PDV of QALYs: • 0% — $200,000 • 3% — $175,722 • 5% — $162,156 • 10% — $135,180

7. Discounting Costs • Two strategies for picking discount rates • Use the market (e.g. long-term bonds) • Use a political process. • Advantage of using the market is that there is a theoretical justification for doing so. • However, market based discount rates may not reflect the type of transactions that are relevant for health care.

8. Discounting QALYs • Some controversy about how (and if) to discount QALYs • Is a future life year worth less than a contemporaneous one? • The prevailing practice is to use the same discount rate for costs as one uses for health consequences. • If discounts rates between costs and health benefits are different then either you want to delay interventions or you want to ‘over implement.’ • Want ‘Horizontal Equity’: (i.e., assuming equal healthcare access to those who are the same in a relevant respect (such as having the same 'need').

9. Issues? • What about inflation? • Analysis should be conducted in ‘real’ terms. • What about uncertainty? • Use ‘certainty equivalence’ in the analysis: (e.g., The amount of payoff (e.g. money or utility) that an agent would have to receive to be indifferent between that payoff and a given gamble is called that gamble's 'certainty equivalent'. For a risk averse agent (as most are assumed to be) the certainty equivalent is less than the expected value of the gamble because the agent prefers to reduce uncertainty.) • Assume risk neutrality to address certainty equivalence • Should we use a constant discount rate? • Although, individuals display ‘time-inconsistency’, it is probably better to use a constant ‘rational’ discount rate. • Keep valuations and discounting separate!

10. Which interest rate? • There are many interest rates out there, which one, if any, is the right one? • Captures the rate at which society is willing to trade off current for future consumption. • Many interest rates contain a risk-premium/inflation premium.

11. Which Interest Rate? • Most CE analysis use 5%. • Empirical literature suggests a somewhat lower rate. • Lind (1982) – 2%; • Lesser and Zerbe (1994) 2.5-5% • UK NHS 6% • World Bank 3% • CDC 5% • Bottom line: Use 3% to 5% • Sensitivity Analysis: 0% to 7%

12. Uncertainty and Risk • There are several sources of uncertainty in CE analysis. • Statistical Uncertainty • Randomness in health & cost outcomes • Analyzing the CE values for alternative parameter values is called Sensitivity Analysis.

13. Statistical Uncertainty • Parameter Uncertainty • Intervention reduces chance of heart attack by 12% with 5% error. • Cost of intervention is $200 with a standard error of $40. • One-way sensitivity analysis changes one variable at a time. • Multi-way changes many at a time—Likely need to do Monte Carlo analysis.

14. Monte Carlo Analysis • Computer simulation methodology to examine the impact of uncertainty. • Use a random number generator. • The parameters of interest in CE analysis will have a given distribution, usually Normal, with a mean and variance. • Each iteration, take a draw from the distribution of parameters. • Calculate the CE ratio given the draw and keep track of it. • The distribution of the CE ratios from all of the iterations is the distribution of the CE ratio