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"Stated Preference Theory (Conjoint Analysis) is the Best Way to Assess
Health-Related Quality of Life for Economic Assessment of Drugs and Medical Interventions: An Application
in Alzheimer Disease"
Joel Hay, PhDDepartment of Pharmaceutical Economics and PolicyUniversity of Southern California
April 21 2006Presented at ISPOR Student Teleconference
Research supported by the State of California Alzheimer Disease Research Centers Network
Overview
Measuring Health Related QOL for
Cost Effectiveness Analysis
Problems with CE ratios in making
decisions
Justifications for Net Monetary
Benefits based on willingness to pay
Overview
Introduction to Conjoint Analysis
Alzheimer Conjoint Experiment Design
Results for the Alzheimer CA Experiment
Conclusions & Future Directions
Stated Preference Elicitation Methods
Important to the design of successful drugs and other interventions
Can reduce R&D costs enormously by focusing on treatment and disease symptoms that are most important
Can be used to weigh utility of outcomes in cost-effectiveness analysis
Stated Preference Methods
Willingness to Pay/ Stated Value
Standard Gamble / Time Trade-off
Global Assessment / Visual Analog
Health Status Assessment / Multi-attribute Scales
Conjoint Analysis/ Discrete Choice Experiments
Stated Preference Methods
Stated preference is not revealed preference
All stated preference methods ask the subject to make hypothetical choices
All stated preference methods require the subject (patient) to consider or imagine the value of health states they don’t actually experience
Denominator of CEA Ratio[Cost(A) - Cost(B)] /
[QALY(A) - QALY(B)]
How are QALYs determined?
Patient or subject survey of health states
EQ-5D, HUI, QWB, SF-##
Results converted into QOL-weighted time
Uses someone’s utility weighting algorithm
Utility translation imprecision is generally ignored
Problems with QALYsDoesn’t accurately reflect individual utilities
Implies that people have constant preference for healthy years
Ignores risky behaviors
Ignores life expectancy constraints
Ignores alternative consumption opportunties
Creates serious analytic problems in a CE ratio
-2000
-1500
-1000
-500
0
500
1000
1500
2000
-0.25 -0.15 -0.05 0.05 0.15 0.25
Difference in effectiveness
Dif
fere
nce
in c
ost
s
-2000
-1500
-1000
-500
0
500
1000
1500
2000
-0.25 -0.15 -0.05 0.05 0.15 0.25
Difference in effectivenessD
iffe
ren
ce in
co
sts
• Feiller’s Theorem Probability Ellipses
STOCHASTIC UNCERTAINTY / VARIABILITY
Computing 95% CI around mean ratio is plagued with many problems
ΔC
ΔE
Analyzing CEA Ratio Results
Computing 95% CI around mean ratio is plagued with many problems
• CI can include undefined values• ΔC / ΔE• What if ΔE = 0?
• CI = (LL, ∞) U (∞, UL)
ΔC
ΔE
Analyzing CEA Ratio Results
Computing 95% CI around mean ratio is plagued with many problems• CI can be undefined
Analyzing CEA Ratio Results
Computing 95% CI around mean ratio is plagued with many problems
• The same value for a CER can
have 2 completely different
meanings
• a1 = a2 but many prefer a2
• b1 = b2 but in reality, b1 << b2
ΔC
ΔE
b2
b1
a2
a1
Analyzing CEA Ratio Results
Computing 95% CI around mean ratio is plagued with many problems
• Not properly ordered outside trade-off quadrants• b better than c• c better than a• a and b equal when in
reality b >> a
ΔC
ΔE
c
b
a
Analyzing CEA Ratio Results
Analyzing CEA Ratio Results
Incremental costs positive
Incremental costs negative
Incremental effects
positive
Incremental effects
negative
Quadrant 4Inferior
Quadrant 1Trade off
Quadrant 2Superior
Quadrant 3Trade off
R
Acceptability Curves
Acceptability CurvesCost Effectiveness Acceptability Curves
Shows the probability that the new therapy will be cost effective as a function of the societal willingness-to pay (for a QALY) thresholdGets around the problems of CEA confidence intervals
Main advantage: Adopts the natural perspective / interpretation of decision makers: “how likely is it that the intervention will be cost effective”
Acceptability Curves
Cost-effectiveness Threshold R
P (
Inte
rven
tio
n i
s C
ost
eff
ecti
ve)
0
1
Proportionof casesIn which
interventionis more effective
(Quadrants 2+1)
Proportionof cases in which the intervention is cost-savings(Quadrants 2+3)
One-sided “p” value
for cost (savings) difference
Curve tend to1-“p” value (one-sided)
for effectivenessdifference
$0 $25,000 $50,000 $75,000 $100,000 $
Acceptability Curves
Analysts have had the natural tendency to interpret the results as the probability that the cost-effectiveness ratio is lower or equal than a specific threshold (given the data)AC are not always monotonically increasing! Their shape depends on the data – they can go up and down!95% intervals cannot always be defined
Using Net Monetary Benefits to Make Decisions
The incr net benefit (INB) was introduced to estimate whether a treatment is cost effective
INB = IC – λ *IE λ is decisionmaker willingness to pay for QALYA therapy, for which the INB is lower than zero, may be considered cost-effective
However, even when acceptability curve indicates that new treatment is cost-effective at a λ the decision maker is willing to pay, there is still a probability that the decision to reimburse the new treatment is wrong
Using Net Monetary Benefits to Make Decisions
Incr Net Ben = Incr Cost – λ * Incr Effect λ reflects the decision maker’s willingness to pay for new therapy
Why are we using subjects to generate HRQOL and QALYs but not using subjects to generate λ (willingness to pay for QALYs)?The CEA researcher is throwing away the opportunity to capture subject willingness to pay FOR NO REASON!!
EXPECTED VALUE OF PERFECT INFORMATION
Is quantifying uncertainty useful to decision makers?
Focus should be on determining the value of collecting new information (= cost of uncertainty)New information valued in terms of expected reductions in decision errors (NMB loss in money)
See Claxton K. The irrelevance of inference: a decision making approach to the stochastic evaluation of health care technologies Journal of Health Economics, 1999;18:341-64
Why not Capture Willingness to Pay Directly?
Gives a more accurate and precise measure of subject trade-off between money and health states
Doesn’t force health utilities into an artificial QOL framework
Allows direct assessment of Net Benefits rather than artificial CEA ratios
Why not Capture Willingness to Pay Directly?
Even if data come from current HRQOL measures it’s possible to develop WTP conversion scales
Need to subject existing HRQOL instruments to WTP assessment
Conjoint Analysis is the way to do this
Advantages of Conjoint Analysis
Rigorous behavioral choice model:
Random Utility Theory
Actually estimates utility parameters
Experimental design efficiently maps choice preferences in multi-attribute space
Subjects can’t “game” responses
Random Utility Maximization
Uiq = Viq + iq
Uiq > Ujq for all j i element of A
Pi = Pr[(Viq - Vjq) > (jq - iq )]
Stated Preference Elicitation in Alzheimer Disease
AD treatments involve trade-offs across different functional domains
Important, since AD patients have difficulty in making & expressing choices
Relies on proxy responders, usually AD caregivers
Stated Preference Elicitation in Alzheimer Disease
Develop a CA experiment to elicit preferences across function domains for hypothetical AD patients
Include treatment costs to estimate willingness-to-pay for improvements/decreases in daily function
Stated Preference Elicitation in Alzheimer Disease
Validate the CA design in a sample of pharmacy students
Apply the CA experiment to a sample of AD caregivers
Compare results
CA ExperimentAttributes & Levels from Health Utilities Instrument-- Mark 3This instrument is widely used and validatedIt was developed to map into a standard gamble utility scale valid for cost effectiveness analysisUtility scores from the HUI can be compared to RUT scores
PILOT STUDY
The CA design was validated in a sample of pharmacy students:
-HUI functional domain attributes were shown to be significant and consistent predictors of choice
- Total HUI utility score is much better predictor of choice than individual components
Survey
Survey•DEMOGRAPHICS
•Survey was administered to 74 AD caregivers enrolled in the California AD Research Centers.
•STATED PREFERENCE SCENARIOS
•HUI-3 for the AD PATIENT, as PROXIED by the CAREGIVER
•HUI-3 for the CAREGIVER
•SF-36 for the CAREGIVER
SurveySTATED PREFERENCE SCENARIOS
Fractional Factorial Experimental Design
Attributes and Levels taken from the Health Utilities Instrument - Mark 3
best – 3rd – worst
243 combinations of comparisons that were not dominant or dominated choices
25 x 10 choice sets each
61 x 4 choice sets each
Cost of each choice = $100 / $50 / $0
Scoring the HUI
• Patient HUI as proxied by the CG
• Caregiver HUI
• HUI of each choice set
Multi-attribute utility function:
u = 0.371(b1*b2*b3*b4*b5*b6*b7*b8)-0.371
Analysis Plan• Demographics
• MNL for HUI, cost in predicting choice
• Strength of each attribute in determining subject choice
• Subject WTP for various choice attributes – determining strength of cost attribute
• Estimate subject utility
- Health utility of AD patients- Mapping of the HUI-3 to the SF-36 for AD
Results:Descriptives
Mean Std. Deviation
Patient HUI 0.35 0.38 Caregiver HUI 0.88 0.13 SF-36 Physical Function 82.00 20.68 SF-36 Physical Role Function 76.47 33.84 SF-36 Bodily Pain 74.09 22.59 SF-36 General Health 76.32 16.54 SF-36 Vitality 63.34 18.76 SF-36 Social Functioning 80.69 20.87 SF-36 Emotional Role Function 73.49 34.78 SF-36 Mental Health 76.68 15.37 Caregiver Age 60.79 14.45 Caregiver Years of Education 15.31 3.68 Caregiver duration of caregiving since disease onset (months)
82.70 136.53
Caregiver hours per week 23.76 32.21 Caregiver is Spouse of Pt 0.40 0.49 Caregiver is Daughter of Pt 0.28 0.45 Caregiver is Son of Pt 0.17 0.38 Working Full Time 0.42 0.49 Retired 0.30 0.46 Primary Caregiver 0.76 0.43 Married 0.73 0.44 White 0.45 0.50 Hispanic 0.31 0.46 Asian 0.11 0.32 Black 0.09 0.29 Gender (1=male) 0.35 0.48
Results: Correlations
Patient HUI Caregiver HUI
Caregiver SF-36 Physical Function
Caregiver SF-36 General Health
Caregiver SF-36 Mental Health
Patient HUI Pearson Correlation
1 0.303 0.097 -0.003 0.115
Sig. (2-tailed) 0.00 0.07 0.96 0.03
Caregiver HUI Pearson Correlation
0.303 1 0.528 0.356 0.335
Sig. (2-tailed) 0.00 0.00 0.00 0.00
Caregiver SF-36 Physical Function
Pearson Correlation
0.097 0.528 1 0.401 0.216
Sig. (2-tailed) 0.07 0.00 0.00 0.00
Caregiver SF-36 General Health
Pearson Correlation
-0.003 0.356 0.401 1 0.511
Sig. (2-tailed) 0.96 0.00 0.00 0.00
Caregiver SF-36 Mental Health
Pearson Correlation
0.115 0.335 0.216 0.511 1
Sig. (2-tailed) 0.03 0.00 0.00 0.00
Results:Regression
Dependent Variable: Patient HUI Unstandardized
Coefficients Sig.
B Std. Error
(Constant) 0.226 0.197 0.253 Caregiver SF-36 Physical Function -0.005 0.001 0.000 Caregiver SF-36 Physical Role Function
-0.004 0.001 0.000
Caregiver SF-36 Bodily Pain 0.005 0.001 0.000 Caregiver SF-36 General Health -0.005 0.001 0.000 Caregiver SF-36 Vitality 0.003 0.002 0.070 Caregiver SF-36 Social Functioning 0.003 0.001 0.015 Caregiver SF-36 Emotional Role Function
0.001 0.001 0.056
Caregiver SF-36 Mental Health -0.005 0.002 0.002 Age -0.002 0.002 0.344 Years of Education -0.004 0.005 0.504 CG duration of caregiving since disease onset (months)
0.000 0.000 0.528
CG hours per week -0.003 0.001 0.000 Caregiver is Spouse of Pt 0.176 0.083 0.034 Caregiver is Daughter of Pt 0.033 0.064 0.605 Caregiver is Son of Pt 0.197 0.086 0.022 Working Full Time 0.012 0.050 0.812 Retired 0.129 0.059 0.029 Primary_CG 0.095 0.054 0.078 Married -0.234 0.057 0.000 White -0.119 0.090 0.185 Hispanic -0.395 0.090 0.000 Asian -0.004 0.101 0.966 Black -0.395 0.111 0.000 Gender -0.078 0.058 0.181 Caregiver HUI 1.261 0.180 0.000
Results•In a multinomial logistic regression, treatment choice was positively related to HUI score for the chosen intervention and negatively related to treatment costs (P < 0.01).
•At the margin, caregivers would be willing to spend an additional $5-$7 per month for any AD intervention that increased patient HUI utility scores by 1%.
•The strongest rankings include improvements in ambulation, emotion and cognition.
Results
•While HUI scores are related to SF-36 scores, the magnitude of response is fairly small.
•Neither HUI score is related to SF-36 Vitality and the Caregiver HUI score is not significantly related to Physical Role Function.
•Regression based on the HUI multi-attribute score was superior to any single-attribute model and non-inferior to models allowing unconstrained parameter weights for each HUI attribute domain.
Conclusions
•Conjoint Analysis is a useful method for benchmarking the potential values for AD treatments trade-offs in terms of their costs and impacts on patient functioning.
•As found in prior studies, HUI is a useful scale for characterizing proxy patient utility levels.
Conclusions•Consistent with utility theory, results show a methodologically independent and innovative validation of the HUI utility scale as a strong predictor of subject health state preferences.
•Demonstrates that we can convert HUI, SF-36 and other HRQOL scales directly into willingness to pay values for alternative health states
•Do not need to increase imprecision and difficulty by using Cost Effectiveness Ratios
Conclusions
•These methods will become even more relevant as we increasingly utilize brain scanning methods to map utility of health states and utility of money directly
•Functional Magnetic Resonance Imaging
•Positron Emission Tomography
SUGGESTED READINGSHandling Variability and Uncertainty (1)Briggs AH, Fenn P. Confidence intervals or sufaces? Uncertainty on the cost-effectiveness plane. Health Econ 1998; 7:723-740.Briggs AH. Handling uncertainty in cost-effectiveness models. Pharmacoeconomics 2000; 17(5):479-500.Clark DE. Computational methods for probabilistic decision trees. Comput Biomed Res 1997; 30(1):19-33.Critchfield GC, Willard KE, Connelly DP. Probabilistic sensitivity analysis methods for general decision models. Comput Biomed Res 1986; 19(3):254-265.Dittus RS, Roberts SD, Wilson JR. Quantifying uncertainty in medical decisions. J Am Coll Cardiol 1989; 14(3 Suppl A):23A-28A.Doubilet P, Begg CB, Weinstein MC, Braun P, McNeil BJ. Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach. Med Decis Making 1985; 5(2):157-177.Glick HA, Briggs AH, Polsky D. Quantifying stochastic uncertainty and presenting results of cost-effectiveness analyses. Expert Rev Pharmacoeconomics Outcomes Res 2001; 1(1):25-36.
SUGGESTED READINGSHandling Variability and Uncertainty (2)
Halpern EF, Weinstein MC, Hunink MG, Gazelle GS. Representing both first- and second-order uncertainties by Monte Carlo simulation for groups of patients. Med Decis Making 2000; 20(3):314-322.
Shaw JW, Zachry WM. Application of probabilistic sensitivity analysis in decision analytic modeling. Fomulary 2002; 37:32-40.
Stinnett AA, Paltiel AD. Estimating CE ratios under second-order uncertainty: the mean ratio versus the ratio of means. Med Decis Making 1997; 17(4):483-489.
Stinnett AA, Mullahy J. Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making 1998; 18(2 Suppl):S68-S80.
Whang W, Sisk JE, Heitjan DF, Moskowitz AJ. Probabilistic sensitivity analysis in cost-effectiveness. An application from a study of vaccination against pneumococcal bacteremia in the elderly. Int J Technol Assess Health Care 1999; 15(3):563-572.
Fenwick E, O'Brien BJ, Briggs A. Cost-effectiveness acceptability curves - facts, fallacies and frequently asked questions. Health Econ. 2004 May;13(5):405-15.
Recommended Books
Hunink M, Glasziou P, Siegel J, et al (2001). Decision Making in Health and Medicine. Integrating Evidence and Values. Cambridge, UK: Cambridge University Press.
Drumond M, McGuire A (Eds) (2001). Economic Evaluation in Health Care: Merging Theory with Practice. New York, NY: Oxford University Press.
Expected Value of Perfect InformationClaxton, K., Sculpher, M. and Drummond, M. (2002) A rational framework for decision making by the National Institute For Clinical Excellence (NICE) . Lancet 360, 711-716.Claxton, K.C., Neuman, P.J., Araki, S.S. and Weinstein, M.C. (2001) The value of information: an application to a policy model of Alzheimers disease. International Journal of Technology Assessment in Health Care 17 (1): 38-55.Claxton, K. (1999a) Bayesian approaches to the value of information: Implications for the regulation of new pharmaceuticals. Health Economics 8, 269-274.
Conjoint AnalysisHay JW: Conjoint Analysis in Pharmaceutical Research. J Managed Care Pharmacy 8:206-9, 2002.Chiou C-F; Hay J; Wallace J, Bloom B; Neumann P, Sullivan S, Yu H-T, Keeler E, Henning J, Ofman J. Development and Validation of A Grading System for the Quality of Cost-Effectiveness Studies. Medical Care 2003; 41:32–44.Dwight-Johnson M, Largomarsino I, Aisenberg E, Hay J. “Understanding Depression Treatment Preferences among Low-Income Latinos using Conjoint Analysis”. Psychiatric Services 2004;55(8):934-37.Johnson FR. Einstein on Willingness to Pay per QALY:Is There a Better Way? Med Decis Mak 2005; 607-8.
Recommended CA BookLouviere JJ, Hensher DA, JD Swait: Stated choice methods : analysis and applications. Cambridge, UK, New York, Cambridge University Press, 2000.
SUGGESTED READINGS