Economic evaluation of health programmes Department of Epidemiology, Biostatistics and Occupational Health Class no. 16: Economic Evaluation using Decision.
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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 modelsPatient-level simulations
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
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
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
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.
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
Extrapolate further into the future
Most trials short-termLong-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
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
Common elements of all decision-analytic models
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.
Expected values
Multiply outcome by probability; See Box 9.3
Stages in development of model
Define decision problemDefine model boundariesStructure the model
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
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
Limitations of Markov models
Memory-less state transition probabilitiesMay be excessively unrealistic
3rd alternative: patient-level simulation
Each individual encounters events with probabilities that can be made path-dependent
Virtually infinite flexibilityBut how to “populate” all model
parameters?
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