Rescuing Clinical Trial Data For Economic Evaluation

1. Rescuing Clinical Trial Data For Economic Evaluation Paul Kind, Ph.D. Jan van Busschbach, Ph.D. Frank de Charro, Ph.D.

2. Overview of the workshop • Frank de Charro: Introduction • Jan van Busschbach: How to shoot straight at the goal • Paul Kind: How to score with more subtle combinations

3. The problem • Many clinical studies do not generate outcomes data that are suitable for economic evaluation • They may include condition-specific measures but these were generally not designed for the type of analysis required • Such studies may include measures of health-related quality of life as secondary endpoints

4. Economic evaluation • Cost Effectiveness Analysis Measure ofeffectiveness can be anything as long as scaling properties are ok • Cost Utility Analysis Measure of effectivenss: QALYs

5. EQ-5D: Descriptive System • Classification of health states using 5 dimensions • mobility • self care • usual activity • pain / discomfort • anxiety / depression • 3 problem levels for each (none / some / extreme) • defines a total of 35 • = 243 health states Pain Self-care Mood Health state Mobility Usual activities

6. EQ-5D questionnaire

7. State A : 1 1 2 2 3 Scoring EQ-5D health states Population TTO weights State B : 1 1 3 2 2

8. The challenge To devise methodologies that can be used to convert outcomes data collected in clinical studies into a form that have the necessary attributes to support economic evaluation (CUA)

9. Clinical analysis/report Secondary data Clinical study Economic analysis / report Primary data Cost-effectiveness Cost-utility Rescuing economic analysis

10. A word of caution • Quality adjustment is one of the most important outcome characteristics • Indirect approaches involve uncertainty and diminish the potential to differentiate between a treatment and its alternativess • Cost effectiveness will be judged taking into account uncertainty and using probabilistic models • So crosswalks are second best but better than no coverage of utility at all

11. The valuation of disease-specific health states • Jan J. v. Busschbach, Ph.D. • Erasmus MC • Institute for Medical Psychology and Psychotherapy • www.busschbach.nl • Sildenafil (Viagra)

12. The effects of Sildenafil in terms QALY’s is complicated • QALYs are measured with standardised and validated quality of life questionnaires • EQ-5D or HUI III were not included • Not sensitive for erectile dysfunction?

13. Clinical outcomes • Gradations erectile dysfunction • were chosen as clinical outcomes • Measured with the IIEF • International Index of Erectile Function • Primary end points: Question 3 and 4 • Ability to attain an erection • Example: During intercourse I am sometimes able to penetrate • Ability to maintain an erection • I can almost never maintain the erection during intercourse after penetration

14. Clinical, disease specific outcomes Goldstein et al., N Engl J Med, 1998

15. How to convert clinical outcomes into QALYs ? • 2 questions 5 answer levels = 25 health states • Why not value the 25 these health states with Time Trade-Off ? • 169 subjects of the general public • Valued the 25 health states with TTO • Individual administration within groups sessions • Validation of procedure in students (group versus individual)

16. 25 Erectile Dysfunction States • During intercourse I am sometimes able to penetrate • I can almost never maintain the erection during intercourse after penetration

17. TTO values have logic structure

18. Transferred clinical outcomes into QALYs On the basis of Goldstein et al., N Engl J Med, 1998

19. QALY league table

20. Healthy No complains Death All complains Disease specific utilities are not equal to generic utilities • Only the disutility of the specific disease is valued • Generic and specific utilities are not on the same scale • Generic top anchor: absence of any impairment • Specific top anchor: absence of specific impairment • Co morbidity might still be present

21. How to interpret disease specific utilities • Value of life years “traded off” in TTO differ • Healthy subject: 1 life year is 1.0 QALY • Sick subject: 1 life year is 0.5 QALY • Life years of healthy persons are more worth than those of sick • Overall health states influence disutility • 20% trade off at 1.00: disutility = 0.20 • 20% trade off at 0.80: disutility = 0.16 • 20% trade off at 0.60: disutility = 0.12 • Raw disease specific trade-off overestimated gains

22. Specific utilities should be corrected for average morbidity • Solution: multiplicative model • Multiply disease specific value with average value • Values have to be multiplied by average value for age group. • For instance in IPSS • male age 55-64: overall QoL utility: 0.81 • Most severe BPH: 0.87 • Male age 55-64 with most severe BPH: 0.81 x 0.87 = .7047 • Maximum gain reduces from • Raw score 1.00 - 0.87 = 0.13 • Adjust score 0.81 - 0.70 = 0.11 • 15 % reduction

23. Rue of thumb • Overestimated CE-ration by 15% using specific utilities • Proposed by Fryback & Lawrence, MDM 1997 • For not completely the same problem… • …for own health states, not imaginable health states

24. We validated the IIEF and the IPSS for the use in economic appraisal Now, IPSS and IIEF has QALY-weights Many other applications possible (health states of…) diabetic foot ulcers Advantage High sensitive disease specific measures for QALY-analysis No need for generic instrument Disadvantages Not directly compatible with generic utilities…. ± 15 % correction needed Conclusion (1)

25. Overestimation? • Does the focus on the disease makes the disutility to high?

26. Crosswalks: recalibrating Paul Kind Visiting Professor University of Uppsala, Sweden PrincipalInvestigator Outcomes Research GroupCentre for Health EconomicsUniversity of YorkEngland pk1@york.ac.uk

27. Recalibration – the task • Source assumed to be a clinical / condition-specific (sensitive) measure • Format • Summary score / Index • Subscale scores / dimension scores • Items (all or selected) • Target assumed to be a generic index weighted using social preferences • Task – to recalibrate source in terms of target

28. Recalibration strategies - direct • Derive direct estimates of social preferences for source index • May require simplification of complex descriptive system • Will have implications for time and resourcing • May conflict with instrument developer agenda

29. Recalibration strategies - indirect • Multiple solutions linking all or part of source instrument with the target index (directly or indirectly) Strategy A Estimate target index from A1 source index A2 sources subscales A3 source items Strategy B Estimate target dimension/levels from B1 source subscales B2 source items

30. Strategy A1 • 25 item condition sensitive instrument with widespread usage in its therapeutic field • Yes/no answers coded to 1/0 • All items assumed equal weight • Summary index • General population survey of circa 1,000 yielded parallel observations with EQ-5D

31. A mean estimated observed EQ-5D’ = 0.9696 – 0.0204* A

32. Issues • Number of observations across “severity” range • Subgroup impact • Age / gender • Significant factors but small effect • Regression on mean observations • Why not micro level ? • Less good fit • Tricky / messy business • May not significantly improve estimation • EQ5D’ = 0.9419 – 0.0163

33. Strategy A3EORTC QLQC-30 • EORTC QLQC-30 is a generic measure of health-related quality of life (HrQoL) in cancer. Version #3 consists of 28 items with a 4-category response and 2 further items (general health and quality of life) are coded on a 7-point response category scale (see selected items below). • Responses are converted into corresponding numeric scores that may be summed to produce a total score. However, QLQC-30 cannot be used in cost-effectiveness analysis because it is not standardized on a value scale where full health = 1 and dead = 0.

34. EORTC QLQC-30selected items

35. Data • Baseline observations from a previously reported study of 177 patients with pancreatic cancer were available for analysis. HrQoL in these patients had been assessed by self-report using both the QLQ-C30 and EQ-5D measures. • Additional baseline data on patients included their Karnofsky Performance Scale rating

36. Methods • The first 28 QLQC-30 items were dichotomised (not at all = 0 ; quite a bit to very much = 1) and these items, together with the uncoded response to item 29 (general health) were entered in a stepwise linear regression in which EQ-5Dindex was the dependent variable.

37. Results • The results of this regression analysis are given in Table 1, showing that only 6 of the QLQC-30 items proved to be significant. • The r2 of 0.490 equates with levels seen in other such calibration studies. Correlation between observed EQ-5Dindex and estimated values was generally high. However, correlation between observed EQ-5Dindex and estimated value amongst female patients is higher than for male patients in the sample (r = 0.725 vs 0.662). Mean differences across all patients was -0.001 • Figure 1 shows the scatterplot of observed EQ-5D and the value derived for each patient using the 6-item QLQC-30 model

38. Table 1 : Coefficients from stepwise OLS regression model

39. Figure 1 : Observed and estimated values for EQ-5Dindex

40. Figure 2 : Mean observed and estimated EQ-5Dindex values for categories of Karnofsky Performance Scale

41. Have a nice walk But also Try to keep going straight if possible www.euroqol.org rabin@frg.eur.nl userinformationservice@euroqol.org