1. Economic evaluation of health programmes Department of Epidemiology, Biostatistics and Occupational Health Class no. 16: Economic Evaluation using Decision Analytic Modelling II Nov 3, 2008

2. Plan of class  Decision-analytic modeling: General considerations  Markov models  Patient-level simulations

3. Measurement vs. Support to decision-making  Classes 1 to 14 had to do with measurement:  Costs  (Outcomes)  Utilities associated with outcomes  Essential for individual studies  Need to integrate results of individual studies, and go beyond, to inform decision-making

4. To inform decision-making, a single study using one set of primary data is not enough  Integrate all relevant evidence Multiple studies Consider all relevant alternatives Extrapolate from intermediate to final endpoints Extrapolate further into the future Make results applicable to decision-making context

5. Multiple studies of effects of an intervention  Results of any one study influenced by:  Sampling variability  Study design details (e.g., inclusion and exclusion criteria, drug dosage)  Contextual factors (e.g., health care system characteristics)  Averaging across multiple RCTs or other comparative studies can help us attain true value

6. Consider all relevant alternatives  Good decision requires considering more alternatives  Individual studies usually consider few alternatives  Ex: Tx of rheumatoid arthritis (RA): NSAIDs vs disease- modifying antirheumatic drugs (DMARDs) vs newer biologics.  Many possible Tx options, including regarding timing of introduction of DMARDs.  Not all trials consider all options. Ex: one trial considers homeopathy vs NSAIDs vs DMARDs.

7. Extrapolate from intermediate to final endpoints  Many trials consider intermediate clinical endpoints:  % reduction in cholesterol level  CD4 count and viral load test for HIV  Change in Health Assessment Questionnaire (HAQ) score for functional disability (RA)  Medication adherence  Distant from outcomes meaningful for decision- making  Need to extrapolate, using other studies

8. Extrapolate further into the future  Most trials short-term  Long-term consequences often relevant  E.g., supported employment, Tx of RA  Modeling can provide plausible range for LT consequences  Extrapolate survival data using various assumptions  Extrapolate using modeling

9. Make results applicable to decision-making context  Economic analysis : costs and consequences under normal clinical practice  O’Brien et al. 95: Adjust for rates of asymptomatic ulcers (Box 5.1)  Make results applicable to other setting  Subgroups with different baseline effects – see Figure 9.2 Do this on basis of plausible clinical explanation, not data mining

10. Common elements of all decision-analytic models

11. Probabilities  Bayesian vs frequentist notions of probability  Frequentist – probability is a measure of the true likelihood of an event. Probability of rolling a 1 with standard die: 1/6  Bayesian – probability is a subjective estimate of the likelihood of an event.  In decision-analytic models, we do not know probabilities in the frequentist sense. So we use expert judgement. Is it a weakness? Not necessarily. May be the best that we can do.

12. Expected values  Multiply outcome by probability;  See Box 9.3

13. Stages in development of model  Define decision problem  Define model boundaries  Structure the model

14. Types of decision-analytic models  3 basic options: –Decision trees –Markov models –Patient-simulation models  Why use a Markov model instead of a decision tree? Decision tree can get too complicated if the sequence of events is too long. –Especially likely to occur when modeling treatment of chronic illness

15. Example:  Welsing, Severens et al. (2006). Initial validation of a Markov model for the economic evaluation of new treatments for rheumatoid arthritis. Pharmacoeconomics 24(10): 1011-1020  Purpose: Initial validation of Markov model to carry out cost-utility analyses of new treatments for treatment of rheumatoid arthritis

21. Limitations of Markov models  Memory-less state transition probabilities  May be excessively unrealistic

22. 3rd alternative: patient-level simulation  Each individual encounters events with probabilities that can be made path- dependent  Virtually infinite flexibility  But how to “populate” all model parameters?