Transcript Types of decision

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
Patient-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 diseasemodifying 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 decisionmaking
 Need to extrapolate, using other studies
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
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 problem
Define model boundaries
Structure 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 probabilities
May be excessively unrealistic
3rd alternative: patient-level
simulation
Each individual encounters events with
probabilities that can be made pathdependent
Virtually infinite flexibility
But how to “populate” all model
parameters?