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 of effectiveness 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
Health multi dimensional
EuroQol Group chose fice dimension
Three levels
243 helath states
The instrument can be used to generate a single index for a health state
Mobility
Usual
activities 22

6. EQ-5D questionnaire

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

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

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
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. Disease specific utilities are not equal to generic utilities
Healthy
Death
No complains
All complains
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 with most severe BPH: 0.81 x 0.87 = .7047
Maximum gain reduces from
Raw score = 0.13
Adjust score = 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. Conclusion (1)
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

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

26. Crosswalks: recalibrating
Paul Kind
Visiting Professor
University of Uppsala, Sweden
Principal Investigator
Outcomes Research Group Centre for Health Economics University of York England

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 0.000 0.943
0.970
1.000
0.931
0.949
2.000
0.893
0.929
3.000
0.873
0.908
4.000
0.900
0.888
5.000
0.817
0.868
6.000
0.872
0.847
7.000
0.820
0.827
8.000
0.806
9.000
0.852
0.786
10.000
0.743
0.766
11.000
0.795
0.745
12.000
0.780
0.725
13.000
0.747
0.704
14.000
0.793
0.684
15.000
0.712
0.664
16.000
0.564
0.643
17.000
0.626
0.623
18.000
0.761
0.602
19.000
0.275
0.582
20.000
0.562
21.000
0.541
22.000
0.521
23.000
0.500
24.000
0.480
25.000
0.460
EQ-5D’ = – * 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’ = –

33. Strategy A3 EORTC 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-30 selected 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 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 = vs 0.662). Mean differences across all patients was
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
Unstandardized
Coefficients
Std. Error
Significance
(Constant)
0.633
.071
.000
Q29
Overall health
0.047
.013
.001
QX3
trouble with short walk
-0.124
.031
QX26 physical family life impact
-0.082
.009
QX5 help with dressing washing
-0.167
.047
QX20 difficulty concentrating
-0.102
.033
.002
QX11 trouble sleeping
-0.086
.032
.007

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. Try to keep going straight if possible
Have a nice walk
But also
Try to keep going straight if possible