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Dealing with Uncertainty in Cost- Effectiveness Analyses Gerald F. Kominski, Ph.D. Professor, Department of Health Services.

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Presentation on theme: "Dealing with Uncertainty in Cost- Effectiveness Analyses Gerald F. Kominski, Ph.D. Professor, Department of Health Services."— Presentation transcript:

1 Dealing with Uncertainty in Cost- Effectiveness Analyses Gerald F. Kominski, Ph.D. Professor, Department of Health Services

2 CE Ratios: Decision Rules 2 EE  C> 0,  E>0 => CE>0 adopt if CE CE<0 always adopt “cost-saving” = dominates  C CE>0 adopt only if savings is worth health cost  C> 0,  E CE<0 never adopt “dominated” IV III I II

3 Consider a New Therapy That Produces a 3-Fold Decrease in Mortality

4 ICER Comparing Treatments A and B Treatment CostEffectiveness A$6,2000.82133 B$10,4000.94080 B-A$4,2000.11947 ICER = $4,200 / 0.11947 = $35,156

5 Problems with Point Estimate of ICER n Does not express variability in the data n May lead to the adoption of options that are { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/11/3317612/slides/slide_5.jpg", "name": "Problems with Point Estimate of ICER n Does not express variability in the data n May lead to the adoption of options that are

6 Dealing With Uncertainty n Confidence Intervals -95% CIs for ICERs -95% confidence ellipsoid n Net Monetary Benefits n Acceptability Curves n Monte Carlo Simulations

7 Confidence Intervals

8 How to Calculate 95% CIs n Fieller’s Theorem and bootstrap methods have been used by various researchers n These methods work when ICERs are confined to one quadrant of the cost-effective plane (as in the previous slide) n Bootstrap methods work even when ICERs are found in 2 or 3 of the 4 quadrants n Fieller’s Theorem leads to results that are difficult to interpret when the effect difference is insignificant, or when there is no significant difference in costs or effect

9 So, Are CIs Worth Calculating? n Probably not, unless your data are limited to one quadrant of the cost-effectiveness plane n Since the early 2000s, researchers have generally abandoned calculation of 95% CIs in favor of alternative methods: -Acceptability Curves and 95% Ellipsoids -Net Monetary Benefits (NMB)

10 ICER Scatterplot and 95% Ellipsoid for Sample Data C6 C2 C5 C4 C3 C1

11 ICER Scatterplot Data, by Component Comp.QuadrantIncr. Eff.Incr. Cost ICER# PointsPercent C1IVIE>0IC 0IC<0 Superior70.07% C2IIE>0IC>0 0IC>0 <100000997099.7% C3IIIIE 10000000% C4IIE>0IC>0 >100000230.23% C5IIIIE<0IC<0 <10000000% C6IIIE 0 Inferior00%

12 Interpretation of ICER Scatterplot What does the above table mean? Quadrants begin at "I" in the upper right, and increment counter-clockwise to "IV" in the lower right. To identify cost-effective points, a different component labeling system is used. Cost-effective points for "B" lie below the WTP line, in components 1-3. Component 1 (C1) is where the comparator is dominant ('Superior'). Component 2 (C2) is where the comparator is more costly, but lies below the WTP. Component 3 (C3) is where the comparator is less costly, but lies below the WTP. Component 4 (C4) is where the comparator is more costly, and lies above the WTP. Component 5 (C5) is where the comparator is less costly, and lies above the WTP. Component 6 (C6) is where the comparator is dominated ('Inferior').

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14 Net Monetary Benefits n Net Monetary Benefits are based on the maximum ICER or WTP : ICER (max) or WTP =  C /  E > 0 {WTP *  E} –  C > 0 n Less commonly expressed as Net Health Benefits: {  C / WTP}  –  E > 0

15 Slope =  E

16 How Do We Generate These Measures of Variability? n If you have individual level data, you can calculate variability directly from the study data n If you are conducting a cost-effectiveness analysis using published or aggregate data, you need to either: -Have data on the variability for each key variable, or -Estimate the variability in each key variable

17 Monte Carlo Simulations n If you don’t have individual level data, you can simulate the variability in key variables using Monte Carlo techniques n Monte Carlo simulation is a parametric technique, so it requires that you either know or guess the type of distribution each key variable comes from -Nonparametric bootstrapping can be used if you have individual level data n TreeAge can produce Monte Carlo simulations, once you specify variable distribution, means, and standard deviations -More about how to do this in the Lab session


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