Genetic Discoveries and the Role of Health Economics

Transcription

1 Genetic Discoveries and the Role of Health Economics Collaboration for Outcome Research and Evaluation (CORE) Faculty of Pharmaceutical Sciences University of British Columbia February 02, 2010

2 Content Health economics Cost effectiveness analysis Objectives, Methods, Input data, Output and their applications Examples Preference elicitation using choice based experiments Objectives, Methods, Input data, Output and their applications Examples

3 Health Economics There are more beneficial programmes than we have resources to do Choices must therefore be made and opportunities (alternatives that we might have done) forgone: Individual decisions, Health care decisions, Macro economic decisions (defence vs. health) Efficient allocation of resources is where opportunity costs are minimised (i.e. we are getting the greatest value from our resources) Health Economics Health Economic Evaluation

4 Resources are limited

5 Economic evaluation Clinical Trials Demonstrate Value Phase I Cost effectiveness Phase II (a&b) Phase III Approval Other studies Reimbursement Access

6 CEA-when is it used? Compare programs in terms of their cost per outcome (effect) Examples Publicly fund a new genetic test or not Should we add rituximab to CHOP chemotherapy Home dialysis vs. hospital dialysis Fund HPV vaccination program or not

7 Incremental Cost-Effectiveness Ratio Compares a specific (new) intervention to a stated alternative (old) intervention (Cost new Cost old ) / (Benefit new Benefit old ) Incremental resources required by the intervention Incremental health effects gained by using the intervention

8 QALY = Quality Adjusted Life Years INDEX ( utility level ) 1 Drug B No Drug Drug A 0 death TIME Adding Life to Years.

9 QALY = Quality Adjusted Life Years 1.0 Quality- Adjusted Life Years-Gained With Program QUALITY OF LIFE (Weights) Without Program 0.0 QUANTITY OF LIFE (Years) Deat h Death

10 Approaches to CEA/CUA Decision analytic approach (Frankenstein) Appropriate course of action amongst alternatives Based on currently available evidence Preferred option based on expected values Explicit acceptance that decisions made under uncertainty Clinical trial approach (Vampire) A focus on estimating and hypothesis testing Patient level costs and outcomes Concentration on few parameters Beware of trial based costs that don t t represent reality Often need to extrapolate beyond duration

11 Assessing Uncertainty -Sensitivity Analysis Univariate and multivariate sensitivity analysis vary values of parameters to determine impact on outcomes Determine model robustness Probabilistic sensitivity analysis Input parameters that are not known with certainty are expressed using probability distributions (e.g. cost of hospitalization, utility of health state, probability of event) The distribution (e.g. mean, variance) of expected costs and outcomes are calculated using simulation techniques (Monte Carlo) Value of Information Cost of incorrect policy decision can be large Further information can be more prudent than making a potentially incorrect decision Calculates max cost of further research

12 2nd order Monte Carlo simulation WELL U W = pwell SICK psick U S = DEAD Residual (#) U D = 0

13 Examples of CEA Cost-effectiveness of CHOP-R chemotherapy for diffuse large B-cell lymphoma. KM Johnston, CA Marra, JM Connors, M Najafzadeh, L Sehn,, SJ Peacock. Submitted to Value in Health, Sep The Economic Value of a Potential Biomarker for Chronic Obstructive Pulmonary Disease. M Najafzadeh, CA. Marra, LD. Lynd, M Sadatsafavi, and DD. Sin. Cost-Effectiveness of a Universal Herpes Zoster Vaccine Strategy. Pharmacoeconomics, Najafzadeh M, Marra CA, Galanis E, Patrick D.

14 Examples of CEA Comparing CHOP vs. CHOP-R as first-line therapy over a maximum15-year time horizon. Event Based Microsimulation Anonymized individual-level data were obtained from the BC Lymphoma Database: 266 HIV-negative individuals initiating treatment with CHOP (between 1997 and 2000) 519 HIV-negative individuals initiating treatment with CHOP-R between 2003 and 2007) Chemotherapy costs were estimated using British Columbia Cancer Agency (BCCA) data: per-patient charges paid by the agency were available

15 Examples of CEA Age>60 years CHOP-R CHOP Incremental Incremental (Cost per difference in outcome) 15-year time horizon Costs $42,528 $33,413 $9,115 Life years $5,064 Disease -Free life years $5,064 Quality -adjusted life years $6,077 The results are related to a model with 15 years time horizon. Both costs and effectiveness are discounted by 3% annual rate.

16 Examples of Economic Evaluation Susceptible to COPD (All individuals, especially smokers) Undiagnosed COPD Develops COPD Detection of COPD at early stage Detection of COPD at late stage False positive diagnosis (due to other respiratory conditions) No COPD True negative diagnosis Prevention Early diagnosis No effective treatment Effective treatment No effective treatment Effective treatment No effective treatment Inappropriate treatment Improved treatments The red arrows show where the potential benefits of a biomarker can be realized.

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18 Preference Elicitation Using Choice Based Experiments Objective: to measure relative preferences of individuals toward different attributes of a good. Theoretical roots Random utility theory: Lancaster theory of value goods are made up of attributes Total utility gained = sum of individual utilities of attributes Treatment options can be described by specific attributes which can be defined by a number of levels Respondents have unique preferences or utilities for each level of each attribute Learn true value of components when respondents are forced to make trade offs 18

19 Methods Two major methods are currently being used: Discrete Choice Experiment Best-Worse Scaling We assume that products or services made up of bundles of attributes Identify the key attributes that define the product Influential in real decision Reasonable complete set Design of choice based questionnaire Usually requires doing focus group, pilot studies, and etc Asks respondents to choose among available options (profiles) considering the attribute levels Sampling strategy

20 Methods We mathematically infer underlying utilities of each attribute by analyzing the choices Relative importance of attributes Willingness to trade off between attributes (MRS) Relative utility scores of different combinations Values for different sub-groups Considerations for questionnaire design Full factorial vs partial factorial design Optimal design: balanced, orthogonal, minimal overlap SAS, Sawtooth, SPSS Choice data analysis Generalized Linear Models (GLM): Nested logit, Mixed logit, etc Latent Class analysis

21 Advantages of DCE Mimics real choice behavior stated preferences Provides policy relevant information Utility of all possible combinations WTP if cost included as an attribute Basic results easy to interpret Well received and understood by policy makers

22 Example of DCE Bayesian and classical estimation of mixed logit: An application to genetic testing. DA. Regier, M Ryan, E Phimister, CA. Marra. Journal of Health Economics, A valuation of patients willingness-to-pay for insulin delivery in diabetes. C. Guimaraes, CA. Marra, L Colley, S Gill, SH. Simpson, GS. Meneilly, RH. Queiroz, LD. Lynd. International Journal of Technology Assessment in Health Care, 2009.

23 Example of DCE A discrete choice experiment (DCE) was used to elicit preferences related to a hypothetical predictive biomarker. We have measured preferences in a sample of representing r current and previous lymphoma patients.

24 Example of DCE: : Sample Questionnaire

25 Example of DCE: : Sample Questionnaire

26 Example of DCE relative preferences estimates for the aggregate sample (N=51) Genetic test attributes Regression coefficient Standard error Untreated responders 5 out of * out of out of out of Unnecessary treatment of of non -responders 5 out of out of out of out of Severity of treatment side effects Mild * Moderate Severe Likelihood of treatment side effects 5 out of * out of out of Cost of genetic test $ $ $1, $1, Turnaround time of genetic test 2 days days days Genetic test procedure Mouth swab * Blood sample Tumor biopsy Bone marrow biopsy Liver biopsy Optout

27 Merci!