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?