1 / 22

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

lee-winters
Download Presentation

Economic evaluation of health programmes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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?

More Related