Introduction to Decision Analysis
Outline
- Introductions
- Motivation
- Examples of Decision Analysis
- Workshop objectives
Course Website
- All course materials (slides, case studies) are posted here.
- Our (likely evolving) schedule will also be posted here, and updated regularly.
Introductions
- Please introduce yourself!
Motivation
The Past Two Decades …
- Cured Hepatitis C
- Significantly reduced incidence of HIV
- Potential cure for relapsed/refractory leukemia & lymphoma
- Perfected vaccines (e.g. HPV vaccine) to prevent diseases such as cervical & other cancers
- Strides in preventing cardiovascular disease
Despite these advances …
- Burden of non-communicable diseases (NCDs) like cancer, cardiovascular disease, and diabetes is growing.
- NCDs are the cause of 74 percent of deaths globally, with most (86%) of those deaths in low- and middle-income countries (LMICs)
- Source: WHO
- Rates also remain high in many parts of the world for malnutrition, unmet need for sexual and reproductive health services, and maternal mortality.
Despite these advances …
Governments cannot afford all the healthcare from which people could possibly benefit
Either implicitly or explicitly, we make choices about which programs to fund, which populations to screen, and which expensive new drugs to provide to which patients
Decision Analysis can help us ensure that we prioritize the highest value care possible at an efficient price point
Decision Analysis
Decision analysis is a methodology that is uniquely beneficial when there are meaningful tradeoffs between healthcare interventions, but the best strategies for obtaining optimal outcomes are uncertain.
Value
Economists have long defined value as “outcomes relative to costs”
> If we only consider benefits when we define value, it’s no different than efficacy or effectiveness research. And we obviously don’t want to just consider costs without benefits!
Examples of Decision Analyses
Ex 1. HIV
You have been appointed as Director of a funding allocation committee responsible for prevention & treatment initiatives for HIV.
How will the committee decide on the proportion of funds for prevention efforts versus treatment?
Should any of the funds be used for research?
How do you respond to a member who argues that the funds are better spent on childhood vaccinations?
Ex 2. Birth Defects
A hypothetical birth defect is present in every 1 in 1,000 children born
Unless treated, this condition has a 50% fatality rate
Ex 2. Birth Defects
Should we test for this hypothetical birth defect?
A hypothetical birth defect is present in every 1 in 1,000 children born.
Unless treated, this condition has a 50% fatality rate.
Ex 2. Birth Defects
Should we test for this hypothetical birth defect?
Diagnostic test: Perfectly accurate
All newborns in whom the defect is identified can be successfully cured
BUT the test itself can be lethal:
- 4 in every 10,000 infants tested will die as a direct and observable result of the testing procedure
Ex 2. Birth Defects
Objective: Minimize total expected deaths
Ex 2. Birth Defects
Objective: Minimize total expected deaths
Consider a population of 100,000 newborns
Testing produces: (0.0004 x 100,000) = 40 expected deaths
No testing produces: (0.001 x 0.5 x 100,000) = 50 expected deaths
Looks like TESTING WINS!
Anyone got a problem with this??
Different lives are lost
With testing, virtually all 40 deaths occur in infants born without the fatal condition.
With no testing, all 50 expected deaths occur from “natural causes” (i.e. unpreventable birth defect)
Different lives are lost
“Innocent deaths” inflicted on children who had “nothing to gain” from testing program
We may treat one child’s death as more tolerable than some other’s – even when we have no way, before the fact, of distinguishing one infant from the other.
Ex 3. Substance use treatment in pregnancy
Another example of “Competing interests”
[Leech AA, 2024]
Ex 3. Opioid/substance use in pregnancy
[Leech AA, 2024]
Ex 3. Opioid/substance use in pregnancy
[Leech AA, 2024]
Ex 3. Opioid/substance use in pregnancy
[Leech AA, 2024]
Ex 3. Opioid/substance use in pregnancy
- The clear winner is buprenorphine!!
- Yes, BUT…
- We know that clinically, patient choice is really important for retention outcomes
- We can also see that our final conclusions are driven by the infant outcomes (methadone actually has better retention outcomes)
- We did not simulate the birthing parent over their lifetime
Ex 3. Opioid/substance use in pregnancy
The appropriate balance of competing interests between the pregnant individual and the infant is an ethical exercise that is beyond the scope of simulation modeling.
Ex 3. Opioid/substance use in pregnancy
Even if buprenorphine is “dominating” in the parlance of decision science and health economics–if requiring this treatment leads to reduced retention–it becomes a poor policy, leading to worse outcomes & higher costs than allowing individuals to CHOOSE their preferred option.
Estimating probabilities is fundamental to decision making
- Cannot readily obtain needed probabilities
- Varying time periods / lengths
- Methods to estimate probabilities
Commonality of cases
- Unavoidable tradeoffs
- Different perspectives may lead to different conclusions
- Multiple competing objectives
- Complexity
- Uncertainty
Decision Analysis
Aims to inform choice under uncertainty using an explicit, quantitative approach
Aims to identify, measure, & value the consequences of decisions under uncertainty when a decision needs to be made, most appropriately over time.
Workshop Design
- We’re flexible – if there is a topic that is unclear to you, or that you would like expanded upon, please let us know!
- Mixed content
- Lectures
- Small group case studies
- Large group case studies and “hands-on” Excel exercises
Workshop Content
- Basics of decision analysis (Day 1)
- Decision trees
Workshop Content
- Basics of Cost-Effectiveness Analysis (Days 2-3)
- Valuing cost and health outcomes
- Incremental cost-effectiveness analysis
- Introduction to Markov Modeling
Workshop Content
- Advanced Topic Preview (4)
- Sensitivity Analysis
- Advanced CEA modeling frameworks.
