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Summary Health Economic Modeling (University of Twente)
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Summary of the course Health Economic Modeling of the master Health Sciences..
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Health Economic ModelingLecture 1 Introduction
Remember of previous years
- Concepts of utilities and QALYs
Utility = the measure of the preference or value that an individual
or society gives a particular health state (often between 0 and 1).
QALYS = the quality-adjusted life year is a generic measure of
disease burden, including both the quality and the quantity of life
lived. One QALY equates to one year in perfect health. 1 year of
life lived in a situation with utility 0.5 yields 0.5 QALYs.
- Estimation of the incremental cost effectiveness ratio (ICER)
ICER: An incremental cost-effectiveness ratio is a summary
measure representing the economic value of an intervention,
compared with an alternative (comparator)
Cost A −Cost B
ICER=
Effect A −Effect B❑
- Simple approaches to modelling (decision tree)
Health economic evaluations involves a comparative analysis of two or
more alternative investment options (two interventions), to perform an
incremental cost-effectiveness analysis
When looking at a new innovation, consider: safety, costs, effectiveness,
willingness of health care professionals and patients to use it, availability,
comparison to previous methods
Types of healthcare decisions
1. Market approval decisions (EU regulations)
2. Market access and pricing decisions, e.g. for new pharmaceuticals
(companies)
3. Reimbursement of new pharmaceuticals and medical devices (ZIN,
national insurance, minister of health)
4. Physicians deciding about medical treatments (physician/patient)
Development to clinical use:
,Hierarchy of clinical evidence:
Expert opinions < non-experimental studies < quasi-experimental studies
< controlled studies without randomization < well performed RCTs <
systematic reviews of well performed RCTs
Development of a value dossier (see figure)
1. Collecting all evidence (systematic review)
2. Synthesizing the evidence meta-analysis, health economic model
3. Interpreting the health economic outcomes
In this subject we focus on step 2 and 3
3 questions
- Why do we allow drugs on the market, and reimburse drugs, for
which effectiveness is uncertain? patients often want every drug
which could work if it is safe, the company has invested a lot of
money in development and we want companies to keep offering
their developments to the country (national interest)
- Which aspects/outcomes typically are the most uncertain when the
cost-effectiveness of a drug is first assessed? (side-)effects and
effectiveness on the long-term
- When is an innovation or drug too expensive? if the benefits are
too small compared to costs or when it is unaffordable (budget
impact too high)
Other major challenge in healthcare for the next decades healthcare
personnel shortage
National healthcare institute (ZIN) uses an evaluation procedure for new
drugs
, - Pharmacotherapeutic evidence (does it work) clinical trial data,
systematic review meta-analysis
- Budget impact analysis (is it very cheap) linear model
- Cost-effectiveness analysis (is it worth the price) advanced model
estimating lifetime health effects and costs
Simulation modeling simulation models can be useful alternatives to
randomized controlled trials (RCTs) for assessing the impact of
interventions, for example when:
- Evidence on long term costs and health effects is required
- Decisions need to be based on incomplete evidence
- Evidence on intermediate outcomes needs to be linked to long-term
health effects and costs
- Health economic results need to be generalized to other
settings/countries
- Interventions need to be compared that were never evaluated
directly with each other
Time horizon in simulation modeling Time period over which the effects
(positive and negative) are calculated. In most situations there are
potential lifelong effects, so a lifetime time horizon is used.
Amount of evidence in simulation modeling First clinical trials assessing
effectiveness typically have very limited follow-up (e.g. 3-12 months. This
gives no information on long-term effects. The data can be extrapolated.
Model complexity in simulation modeling determining optimal model
complexity can be challenging. Too simple models don’t adequately reflect
evidence or clinical practice, while too complex models are hard to
understand and may require too many assumptions.
, Lecture 2
Example of a simple Markov Model:
Transition matrix of a Markov Model:
Example of a Markov model with cohort simulation:
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