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Economic evaluation of health programmes

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. Plan of class. Decision-analytic modeling: General considerations Markov models

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Economic evaluation of health programmes

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  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

  16. Limitations of Markov models • Memory-less state transition probabilities • May be excessively unrealistic

  17. 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?

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