What is cost-benefit analysis? What are its strengths & limitations? Explain.
💰 What is Cost–Benefit Analysis (CBA)?
Cost–benefit analysis is a way of deciding whether something is worth doing by comparing how much it costs with how much benefit it provides — with both measured in money.
In this method, all outcomes (both costs and benefits) are converted into dollar values. This allows researchers or policymakers to directly compare whether the benefits are greater than the costs.
A key concept in cost–benefit analysis is the net benefit (NB):
Net Benefit = Total Benefits - Total Costs
- If NB > 0, the benefits are greater than the costs, so the intervention is considered worth doing.
- If NB < 0, the costs are greater than the benefits.
⚖️ Strengths
- The results are easy to interpret because everything is measured in the same unit (money).
- If the extra benefits of a treatment are worth more than the extra costs, it clearly shows the intervention is economically worthwhile.
⚠️ Limitations
- It can be very difficult to assign a dollar value to health outcomes, such as improved quality of life or longer survival.
- There may be ethical concerns, because valuing health outcomes in dollars can depend on factors like ability to pay, which raises fairness issues.
🏥 Clinical Example (Psychiatry)
Researchers evaluate whether funding an early intervention program for psychosis is worthwhile.
The program costs $10,000 per patient but reduces hospital admissions, unemployment, and long-term disability costs by $35,000 per patient. Because the financial benefits exceed the costs, the program has a positive net benefit and is considered economically worthwhile.
🧠 Explain it to a 10-year-old
Imagine buying a lemonade stand for $20.
If you sell lemonade and make $50, you earned more money than you spent, so it was a good idea to buy the stand.
🎣 Memory Hook
“Cost–benefit analysis asks the simplest question in economics: Did the health idea make more money than it ate for lunch? 💸🍔”
What is a cost-utility analysis? What are its strengths & limitations? Explain.
(Note: The image you provided actually describes Cost–Utility Analysis (CUA), not cost-benefit analysis. I will explain cost-utility analysis using the information from the image.)
💊 What is Cost–Utility Analysis (CUA)?
Cost–utility analysis is a type of economic evaluation that compares the cost of a treatment with the health benefit it produces, where the benefit is measured using quality-adjusted life years (QALYs).
A QALY combines two things:
- How long someone lives
- How good their health or quality of life is
In cost–utility analysis, the effect of a treatment is measured in QALYs, meaning the treatment is evaluated based on how much it improves both quality of life and length of life.
📊 Why is this useful? (Strengths)
- It allows researchers to include both quality of life and length of life in the same measure.
- Because QALYs are intended to be a universal unit, very different health programs (for example, cancer treatment vs mental health treatment) can theoretically be compared using the same metric.
⚠️ Limitations
- QALY measurements can vary depending on the method used to calculate them.
- Different people may rate quality of life differently, which can change the QALY estimate.
- Society may value some health states differently than others, raising ethical questions.
For example, some people may value improvements in certain conditions (such as physical problems) differently from improvements in severe mental illness.
🧠 Clinical Example (Psychiatry)
Researchers compare long-acting injectable antipsychotics vs oral medication for schizophrenia.
The injectable treatment reduces relapse and improves daily functioning, resulting in more QALYs gained, but it also costs more. Cost-utility analysis determines whether the extra QALYs gained are worth the additional cost.
🧒 Explain it to a 10-year-old
Imagine two medicines help people live longer.
But one medicine also helps them feel much happier and healthier while living those years. Cost-utility analysis tries to measure how good those extra years of life are, not just how long they last.
🎣 Memory Hook
“Cost-utility analysis asks: not just How long did you live? but How good were those years? — the ‘life quality scorecard’ of medicine.” ⏳✨
What is cost-effectiveness analysis? What are its strengths & limitations? Explain.
💊 What is Cost-Effectiveness Analysis (CEA)?
Cost-effectiveness analysis is a way of comparing treatments to see which one gives the best health outcome for the money spent.
In this type of economic evaluation, the effect of a treatment is measured using one outcome in its natural units, rather than converting everything into dollars or QALYs.
Examples of natural units include:
- Number of depression relapses prevented
- Life-years gained
- Blood pressure reduction
- Hospital admissions avoided
Researchers compare how much each treatment costs and how much improvement it produces in that specific outcome.
📊 Strength
- The analysis focuses on one clear outcome measured in its natural units, which makes it relatively straightforward to understand and interpret.
⚠️ Limitation
- Because only one outcome is measured, other important effects of the treatment may be ignored, even though they could still be clinically relevant.
🏥 Clinical Example (Psychiatry)
Researchers compare cognitive behavioural therapy (CBT) with antidepressant medication for major depressive disorder.
They measure the outcome as number of depressive episodes prevented per year. Cost-effectiveness analysis compares how much each treatment costs for each episode prevented.
🧠 Explain it to a 10-year-old
Imagine you want to buy a toy that throws the most water balloons for the money.
If one toy costs $10 and throws 50 balloons, and another costs $20 but throws 60 balloons, you compare how many balloons you get for the price.
🎣 Memory Hook
“Cost-effectiveness analysis asks: Which treatment gives the biggest health bang for the buck? 💥💰”
What is cost-minimisation analysis? What are its strengths & limitations? Explain.
💰 What is Cost-Minimisation Analysis (CMA)?
Cost-minimisation analysis is a type of economic evaluation used when two or more treatments are known to produce the same clinical outcome.
Because the treatments work equally well, researchers do not need to measure the health effects. Instead, they simply compare the costs of the treatments and choose the cheapest option.
So in cost-minimisation analysis:
- No effect outcomes are measured, because the treatments are assumed to have identical effectiveness.
- Researchers only need to collect cost data to determine which treatment is least expensive.
📊 Strength
- The analysis is simple, because researchers only need to gather cost information, not detailed health outcome data.
⚠️ Limitations
- In reality, very few treatments produce exactly identical outcomes, so this method is rarely appropriate.
- Researchers usually still need to collect evidence showing the treatments truly have equal effects before they can assume this.
🏥 Clinical Example (Psychiatry)
A hospital compares two generic brands of sertraline used to treat depression.
Since both medications contain the same active drug and produce the same clinical effect, the hospital performs a cost-minimisation analysis and chooses the cheaper brand.
🧠 Explain it to a 10-year-old
Imagine two ice-cream shops sell exactly the same chocolate ice-cream.
If they taste the same and are the same size, you would just buy the cheaper one.
🎣 Memory Hook
“Cost-minimisation analysis says: If the treatments work the same, pick the cheapest one and keep the change.” 🪙
What is the incremental cost effectiveness ratio (ICER)? Compare it to the CEA (cost-effectiveness analysis).
💊 What is the Incremental Cost-Effectiveness Ratio (ICER)?
The incremental cost-effectiveness ratio (ICER) is a calculation used in health economics to determine how much extra cost is required to gain one additional unit of health benefit when switching from one treatment to a better one.
It compares two treatments directly and asks:
> How much extra money do we need to spend to get one extra unit of health improvement?
The formula is:
(See attached)
So ICER tells us the extra cost per extra benefit gained when moving to the new treatment.
📊 How ICER compares to Cost-Effectiveness Analysis (CEA)
Cost-Effectiveness Analysis (CEA) is the overall method used to compare treatments based on cost and a health outcome measured in natural units (e.g., life-years gained, hospitalisations prevented, depression episodes avoided).
ICER is the key calculation used within CEA.
Think of it this way:
- CEA → the overall framework for comparing treatments
- ICER → the specific number that tells us how much extra benefit costs
In other words:
CEA asks:
👉 Which treatment gives better outcomes for the cost?
ICER answers:
👉 Exactly how much extra we pay for each extra unit of improvement.
🏥 Clinical Example (Psychiatry)
Researchers compare standard antidepressant treatment with antidepressants plus cognitive behavioural therapy (CBT).
Combined treatment costs $2,000 more per patient but prevents 0.5 additional depressive relapses per year.
The ICER shows the extra cost required to prevent one additional relapse.
🧠 Explain it to a 10-year-old
Imagine two video games.
Game A costs $10 and gives 10 hours of fun. Game B costs $20 and gives 15 hours of fun. ICER asks: how much extra money did you spend for each extra hour of fun?
🎣 Memory Hook
“ICER asks: How much extra cash for the extra health splash? 💸💦”
Calculate the ICER for Intervention 1.
Intervention 1
Cost (C1) = $125,000
Effect (E1) = 2000
Intervention 2
Cost (C2) = $100,000
Effect (E1) = 1000
See attached image.
There are some limitations for cost-effectiveness analysis.
Here are 3 common ones:
1. CEA works best when treatments measure the same outcome
2. CEA results depend on how good the data provided are
3. CEA does is not the only criteria that should inform a purchasing decision.
Explain the meaning of the first limitation.
1️⃣ CEA works best when treatments measure the same outcome
CEA compares treatments using one specific outcome measured in natural units, such as:
- Life-years gained
- Relapses prevented
- Blood pressure reduced
This makes it easy to compare similar interventions.
🧠 The problem
CEA becomes less useful when comparing completely different areas of healthcare, because the outcomes may be measured differently.
For example:
Since the units are different, it becomes hard to compare which treatment is the best use of money.
📌 What researchers do instead
To compare across different health conditions, researchers often use:
- Cost-utility analysis (CUA) → uses QALYs (a common unit combining life length + quality of life)
- Cost-benefit analysis (CBA) → converts everything into money
🏥 Clinical Example
A government wants to decide whether to fund:
- a new cancer drug (measured in life-years gained)
- CBT for depression (measured in depression relapses prevented)
CEA cannot easily compare these because the outcomes are different, so policymakers may use QALYs instead.
🧒 Explain it to a 10-year-old
Imagine comparing:
- How many apples a tree grows
- How fast a car drives
Both are measurements, but they are totally different things, so it’s hard to decide which one is “better.”
Treatment | Outcome measured |
| —————— | —————————– |
| Cancer treatment | Life-years gained |
| Depression therapy | Depressive episodes prevented |
| Hip replacement | Mobility improvement |
There are some limitations for cost-effectiveness analysis.
Here are 3 common ones:
1. CEA works best when treatments measure the same outcome
2. CEA results depend on how good the data provided are
3. CEA does is not the only criteria that should inform a purchasing decision.
Explain the meaning of the second limitation.
2️⃣ The results of CEA depend on how good the data is
CEA relies heavily on the quality of the clinical studies used to estimate treatment effectiveness.
If the evidence about how well a treatment works is poor or uncertain, the cost-effectiveness results may also be unreliable.
Because of this, researchers perform sensitivity analysis.
What sensitivity analysis means
Researchers test what happens if their assumptions change.
For example:
- What if the treatment works less well than expected?
- What if the cost increases?
If the results stay similar under different assumptions, the findings are more reliable.
🏥 Clinical Example
A study finds a new antipsychotic appears cost-effective.
Researchers then test what happens if the drug price increases or relapse reduction is smaller than expected. If the drug is still cost-effective, the result is considered robust.
🧒 Explain it to a 10-year-old
If you think a toy will last 10 years, it might seem like a good deal.
But if it only lasts 2 years, it might not be worth the money. Sensitivity analysis checks what happens if your guess was wrong.
There are some limitations for cost-effectiveness analysis.
Here are 3 common ones:
1. CEA works best when treatments measure the same outcome
2. CEA results depend on how good the data provided are
3. CEA does is not the only criteria that should inform a purchasing decision.
Explain the meaning of the third limitation.
3️⃣ Cost-effectiveness is not the only thing that matters
Even if a treatment is cost-effective, decision-makers still need to consider other factors, such as:
- Fairness (equity)
- Patient needs
- Healthcare priorities
- Ethical concerns
Healthcare decisions are not just about money.
🏥 Clinical Example
A rare disease treatment may be very expensive per patient and not cost-effective, but a government may still fund it because:
- the disease is severe
- few alternative treatments exist
- helping these patients is considered ethically important
🧒 Explain it to a 10-year-old
Even if one toy is cheaper, you might still choose another toy if your friend really needs it more.
What are the advantages of economic analysis?
💰 Advantages of Economic Analysis in Healthcare
Economic analysis helps healthcare systems decide how to spend limited money in the best possible way. Instead of guessing, it provides structured comparisons of costs and health outcomes.
Below are the key advantages explained clearly.
1️⃣ It systematically compares costs and outcomes
Economic analysis evaluates both the costs of an intervention and the health results it produces at the same time.
This makes decisions more logical because we are not just asking “Does this treatment work?”, but also “Is it worth the money?”
🏥 Clinical example
Researchers compare two antipsychotic medications. One works slightly better but costs much more. Economic analysis examines both the improvement in symptoms and the extra cost to determine whether the extra benefit is worth paying for.
🧒 Explain to a 10-year-old
Imagine buying a bike. You don’t just look at how cool it looks, you also look at how much it costs and how well it rides.
2️⃣ It helps answer the key question: “Is the intervention worth the cost?”
Economic analysis helps determine whether:
- A treatment provides enough benefit to justify its cost
- One treatment is better value than another
This helps policymakers choose the most cost-effective option, not just the cheapest.
🏥 Clinical example
A hospital compares online CBT therapy vs face-to-face therapy. If online CBT gives similar outcomes but costs much less, economic analysis may show it is better value for money.
3️⃣ It provides clear numbers to guide decisions
Economic analysis produces measurable results, such as:
- Cost per life-year gained
- Cost per QALY gained
These numbers help decision-makers compare treatments objectively.
For example, a treatment might cost $50,000 per additional QALY gained, which helps governments decide whether it is economically reasonable.
🏥 Clinical example
A new antidepressant improves quality of life but costs much more than existing drugs. Economic analysis calculates the cost per QALY, helping policymakers decide whether the improvement justifies the price.
4️⃣ It improves advocacy in healthcare
Economic analysis helps healthcare professionals argue for funding of important services.
When policymakers see clear evidence that a treatment improves health and saves costs, they are more likely to support it.
🏥 Clinical example
Evidence shows that early intervention programs for psychosis reduce hospital admissions and long-term disability costs. Economic analysis helps justify funding these programs.
5️⃣ It makes decision-making transparent
Economic analysis shows how and why decisions are made, rather than leaving decisions vague or subjective.
This transparency helps ensure decisions are fairer and more accountable.
🧒 Explain to a 10-year-old
Instead of choosing something randomly, you show your maths and explain why you picked it.
6️⃣ It helps set healthcare priorities
Healthcare budgets are limited. Economic analysis helps identify which treatments produce the greatest health benefit for the resources available.
This helps governments decide which programs to fund first.
🏥 Clinical example
A health system must decide between funding:
- a new cancer drug
- mental health prevention programs
Economic analysis helps determine which option produces more overall health benefit for the population.
🧠 Memory Hook
“Economic analysis is healthcare’s shopping calculator — it checks the price, the benefit, and picks the biggest health bargain.” 🛒💊
What are the limitations of economic analysis?
⚠️ Limitations of Economic Analysis in Healthcare
Economic analysis helps compare treatments using costs and health outcomes, but it also has important limitations. It can guide decisions, but it cannot make the decision on its own.
Below are the key limitations explained clearly.
1️⃣ It gives a price tag, but decision-makers still must decide if it is worth it
Economic analysis often summarises results as a number such as:
- Cost per QALY gained
- ICER (incremental cost-effectiveness ratio)
This tells policymakers how much extra benefit costs, but it does not automatically tell them whether the extra benefit is worth paying for.
In other words, the analysis provides information, but human judgement is still required.
🏥 Clinical example
A new antipsychotic costs $120,000 per extra QALY gained compared with older medications.
Economic analysis shows the price of the extra benefit, but policymakers must still decide whether that benefit is worth the cost.
🧒 Explain it to a 10-year-old
Imagine someone tells you a toy costs $100 for a small improvement over your current toy.
You still have to decide whether that improvement is worth the money.
2️⃣ It does not directly consider the available budget
Economic analysis tells us value for money, but it does not always consider whether the health system can actually afford the treatment.
A treatment might be cost-effective, but still too expensive for the total healthcare budget.
🏥 Clinical example
A new therapy for schizophrenia might be cost-effective per patient, but if thousands of patients need it, the total cost could be too high for the healthcare system to fund.
🧒 Explain it to a 10-year-old
Something might be a good deal, but if you only have $10 in your wallet, you still cannot buy the $50 item.
3️⃣ It can oversimplify complex health decisions
Economic analysis reduces complex health outcomes into numbers, such as costs, QALYs, or ICER values.
However, healthcare decisions involve many other factors:
- Severity of illness
- Fairness and equity
- Patient preferences
- Ethical considerations
By focusing mainly on numbers, economic analysis may oversimplify important aspects of healthcare.
🏥 Clinical example
A rare disease treatment may look not cost-effective because it helps only a small number of patients.
However, policymakers might still fund it because the disease is severe and life-threatening.
🧒 Explain it to a 10-year-old
If you choose toys only by price and size, you might ignore the one that is really special to someone.
🧠 Memory Hook
“Economic analysis gives the price tag for health — but doctors and governments still have to decide if it’s worth opening the wallet.” 💸🏥
What is a QALY?
🧮 What is a QALY (Quality-Adjusted Life Year)?
A QALY is a measure used in health economics to combine how long someone lives and how good their health is during that time into a single number.
It helps researchers compare the benefits of different medical treatments.
- 1 year of life in perfect health = 1 QALY
- 1 year of life with reduced health = less than 1 QALY
So QALYs take into account both:
- Quantity of life → how many years someone lives
- Quality of life → how healthy or functional those years are
This allows researchers to measure the overall health benefit of a treatment.
📊 How the worked example works
The slide gives an example of a treatment that extends life but slightly reduces quality of life.
Step 1: Life extension
Without the treatment, the patient would die within 1 year.
With the treatment, the patient lives 4 extra years.
Step 2: Quality of life during those years
However, the patient’s health is not perfect.
Instead of a quality score of 1.0, their quality of life is 0.6.
So each year lived counts as 0.6 QALYs.
Step 3: Calculate QALYs from the extra years
The patient lives 4 extra years at 0.6 quality of life.
[4 \times 0.6 = 2.4 \text{ QALYs}]
So the extra years produce 2.4 QALYs.
Step 4: Subtract the QALY that would have happened anyway
Even without treatment, the patient would have lived 1 year, but with the same reduced quality (0.6).
[1 \times 0.6 = 0.4]
That means 0.4 QALYs would have happened anyway.
Step 5: Calculate the QALYs gained from treatment
[2.4 - 0.4 = 2.0]
So the treatment generates 2 additional QALYs.
🏥 Additional Clinical Example
A new antidepressant improves quality of life from 0.5 to 0.8 for patients with severe depression and allows them to remain well for 5 years.
The treatment therefore produces a large increase in quality-adjusted life years, because patients live the same length of time but with much better functioning and wellbeing.
🧒 Explain it to a 10-year-old
Imagine each year of life is like a battery.
- If you feel perfectly healthy, the battery is 100% full (1 point).
- If you feel only okay, the battery might be 60% full (0.6 points).
QALYs count how many good-quality battery years you have.
🧠 Memory Hook
“QALY = Quality × Life — it’s the health score that asks not just how long you live, but how good those years feel.” ⏳✨
What are the advantages of using QALYs in economic analyses?
⭐ Advantages of Using QALYs (Quality-Adjusted Life Years) in Economic Analysis
QALYs are widely used in health economics because they provide a simple way to measure the overall benefit of medical treatments. They combine how long people live and how good their health is into one number.
Below are the main advantages explained clearly.
1️⃣ QALYs combine quantity of life and quality of life
One of the biggest advantages of QALYs is that they combine two important outcomes into one measure:
- Quantity of life → how long someone lives
- Quality of life → how healthy or functional they are during those years
This makes it easier to measure the total benefit of a treatment.
🏥 Clinical example
A treatment for bipolar disorder may not increase life expectancy but may greatly improve daily functioning and quality of life. QALYs capture this improvement, whereas simple survival measures would not.
🧒 Explain it to a 10-year-old
Imagine rating every year of life like a game score.
If you feel great, that year gets 10 points, but if you feel sick a lot, it gets fewer points. QALYs add up your total life score.
2️⃣ QALYs allow comparisons between treatments
Because QALYs use a standard measurement, they allow researchers to compare different healthcare treatments.
This means treatments within the same disease area can be compared using the same outcome measure.
🏥 Clinical example
Researchers can compare whether CBT or antidepressants produce more QALYs for depression treatment, helping determine which intervention provides better overall benefit.
🧒 Explain it to a 10-year-old
If everyone uses the same scoring system, it becomes easier to compare who won the game.
3️⃣ QALYs help make healthcare decisions clearer and more transparent
When policymakers see results such as cost per QALY gained, it becomes easier to understand what health benefit is being purchased with healthcare spending.
This helps make decisions about healthcare more explicit and evidence-based.
🏥 Clinical example
A government may see that one mental health program costs $20,000 per QALY, while another costs $120,000 per QALY, making it clearer which program provides better value.
🧒 Explain it to a 10-year-old
It’s like showing the scoreboard during a game so everyone knows who is winning and why.
4️⃣ QALYs help set healthcare priorities
Healthcare resources are limited. QALYs help decision-makers identify which treatments produce the most health improvement for the resources available.
This helps governments decide which healthcare programs to fund first.
🏥 Clinical example
A health system may compare early psychosis intervention programs with other services and prioritise the ones that generate the largest number of QALYs for the population.
🧒 Explain it to a 10-year-old
If you only have money for one big toy, you choose the one that will give the most fun for the longest time.
🧠 Memory Hook
“QALYs are healthcare’s scoreboard — they add up how long you live, how good you feel, help compare treatments, and show where the health points are best spent.” 🧮🏥
What are some disadvantages of QALYs?
⚠️ Disadvantages of Using QALYs (Quality-Adjusted Life Years)
QALYs are useful in health economics, but they also have several important limitations. These mostly relate to how QALYs are measured and how accurately they represent real patients’ experiences.
1️⃣ QALY values may not reflect what patients actually value
QALY scores are often based on surveys of the general population, not the patients receiving the treatment.
Because of this, the quality-of-life scores used in QALYs may not match what patients themselves think is important.
For example, someone living with a chronic illness may value certain health states differently than people who have never experienced that illness.
🏥 Clinical example
People without depression might rate severe depression as having a very low quality of life score, while patients living with depression may still find meaningful activities and rate their quality of life higher than expected.
🧒 Explain it to a 10-year-old
Imagine someone guessing how fun your favourite game is without ever playing it.
Their guess might be very different from what you think.
2️⃣ QALYs may miss important differences within a disease
QALYs simplify health outcomes into a single number, which can hide meaningful clinical differences.
Small but important improvements in symptoms, functioning, or wellbeing may not appear clearly in QALY calculations.
🏥 Clinical example
Two treatments for schizophrenia may produce similar QALY scores, but one might greatly improve social functioning or independence, which may not be fully captured in the QALY estimate.
🧒 Explain it to a 10-year-old
If you rate all movies using only one score, you might miss the difference between a movie that is funny, exciting, or really meaningful.
3️⃣ QALY data may not apply to every population
QALY estimates often come from research studies or specific populations, which may not represent the patients being treated in real life.
This means the results might not always apply to different countries, cultures, or patient groups.
🏥 Clinical example
A QALY estimate for antidepressant treatment based on studies in young adults may not accurately represent older adults with multiple medical conditions.
🧒 Explain it to a 10-year-old
If you test a toy with only one group of kids, it might not work the same way for all kids everywhere.
🧠 Memory Hook
“QALYs can be a shaky scoreboard — the scores might not match patients’ feelings, miss small differences, and come from the wrong players.” 🧮🎮
What is the incremental net benefit? How do you calculate it?
💰 What is Incremental Net Benefit (INB)?
Incremental Net Benefit (INB) is a measure used in economic evaluations (especially cost-benefit or cost-effectiveness analyses) to determine whether a new treatment provides benefits that are worth its additional cost.
It works by converting the health benefit into money, and then comparing that value with the extra cost of the treatment.
In simple terms, INB asks:
> Is the value of the extra health benefit greater than the extra cost of the treatment?
If the answer is yes, the treatment is considered worth the cost.
🧮 How to calculate Incremental Net Benefit
The calculation has three main steps.
Step 1 — Calculate the extra health effect (ΔE)
This is the additional benefit produced by the new treatment compared with the standard treatment.
Example outcomes might include:
- additional QALYs gained
- life-years gained
- relapses prevented
Step 2 — Assign a willingness-to-pay value (λ)
Society decides how much it is willing to pay for one unit of health benefit (for example one QALY).
This is called the willingness-to-pay threshold (λ).
Example:
- Society may decide it is willing to pay $50,000 per QALY gained.
Step 3 — Convert the extra health benefit into money
Multiply the extra health effect (ΔE) by the willingness-to-pay value (λ).
[{Monetary value of benefit} = \Delta E \times \lambda]
Step 4 — Subtract the extra cost of the treatment (ΔC)
Finally, subtract the extra cost of the new treatment.
[{Incremental Net Benefit (INB) = (ΔE × λ) − ΔC}]
📊 How to interpret the result
- INB > 0 → the value of the extra health benefit is greater than the extra cost
→ the treatment is considered cost-effective - INB < 0 → the treatment costs more than the value of the benefit
🏥 Clinical Example (Psychiatry)
A new psychotherapy program for depression produces 0.3 additional QALYs per patient compared with usual care.
If society is willing to pay $50,000 per QALY, the benefit is worth $15,000. If the program costs $8,000 more, the INB is positive, meaning the treatment is considered good value.
🧒 Explain it to a 10-year-old
Imagine a game upgrade costs $10, but it gives you extra features that you think are worth $20.
If the upgrade gives more value than it costs, then it was worth buying.
🧠 Memory Hook
“INB asks the ultimate health economics question: Is the health gain worth more money than it costs? 💸⚖️”
What is the cost effectiveness acceptability curve? How would you interpret the example provided?
📈 What is a Cost-Effectiveness Acceptability Curve (CEAC)?
A Cost-Effectiveness Acceptability Curve (CEAC) is a graph used in economic evaluations to show how likely it is that a treatment is cost-effective compared with another treatment.
Because economic studies often involve uncertainty (for example about costs or treatment effects), the CEAC helps show the probability that a treatment is cost-effective at different willingness-to-pay thresholds.
A CEAC is usually shown as a graph with:
- X-axis: how much society is willing to pay for one unit of health benefit (e.g., cost per QALY)
- Y-axis: the probability that the treatment is cost-effective
The curve therefore answers the question:
> If society is willing to pay a certain amount for health benefit, how likely is it that this treatment is good value?
CEACs were introduced as an alternative to simply calculating confidence intervals around the ICER, because they better show uncertainty.
📊 How to interpret the image
The graph in the image shows:
X-axis
Threshold willingness-to-pay per QALY (£)
This is the amount a health system is willing to spend for one additional QALY.
Y-axis
Probability that the treatment is cost-effective
This represents the chance that the treatment provides good value for money.
Key points shown in the image
1️⃣ At £10,000 per QALY
The probability that the treatment is cost-effective is about 20%.
➡️ This means if the healthcare system only wants to pay £10,000 per QALY, the treatment is unlikely to be cost-effective.
2️⃣ As willingness-to-pay increases
The curve rises, meaning the probability that the treatment is cost-effective increases.
This happens because the health system becomes more willing to pay for the health benefit.
3️⃣ Around £40,000–£50,000
The probability is about 80%.
➡️ At this level of willingness-to-pay, the treatment is very likely to be cost-effective.
The graph also notes that the probability is similar between £40k and £50k, meaning increasing spending beyond that point does not change the conclusion much.
🧮 How CEACs are calculated (simplified)
CEACs are usually generated using simulation methods such as bootstrapping.
The process works like this:
1️⃣ Researchers repeatedly simulate many possible cost and effect outcomes from the data.
2️⃣ For each simulation, they calculate whether the treatment is cost-effective at a specific willingness-to-pay threshold.
3️⃣ They repeat this thousands of times.
4️⃣ The proportion of simulations where the treatment is cost-effective becomes the probability shown on the CEAC.
5️⃣ These probabilities are plotted across different willingness-to-pay thresholds to create the curve.
🏥 Clinical Example (Psychiatry)
Researchers evaluate internet-based CBT for depression vs face-to-face CBT.
A CEAC shows that if the healthcare system is willing to pay $20,000 per QALY, internet CBT has a 65% chance of being cost-effective compared with traditional therapy.
🧒 Explain it to a 10-year-old
Imagine buying a toy, but you are not sure if it’s worth the money.
A CEAC is like a chart that shows how likely the toy is to be a good deal depending on how much money you are willing to spend.
🧠 Memory Hook
“CEAC = the ‘confidence curve’ of cost-effectiveness — it shows how likely a treatment is a good deal as your wallet opens wider.” 💸📈
What is ‘boot-strapping’ with relevance to cost-effectiveness acceptability curves?
🧮 What is Bootstrapping?
Bootstrapping is a statistical method used to estimate uncertainty in study results by repeatedly resampling the data.
Instead of collecting new data, researchers take the existing dataset and repeatedly draw many samples from it (with replacement) to simulate thousands of possible results.
This helps estimate how much the results might vary if the study were repeated.
Bootstrapping is often used to construct confidence intervals, showing the likely range of the true effect.
📊 How bootstrapping is related to Cost-Effectiveness Acceptability Curves (CEACs)
In cost-effectiveness studies, researchers use bootstrapping to generate many possible combinations of cost and health outcomes.
Each simulated dataset produces a slightly different estimate of:
- cost differences
- health benefit differences (e.g., QALYs)
These thousands of simulated results are then used to calculate how often a treatment appears cost-effective at different willingness-to-pay thresholds.
Those probabilities are plotted to create the Cost-Effectiveness Acceptability Curve (CEAC).
So in simple terms:
- Bootstrapping → generates many simulated study results
- CEAC → graph showing how often those results suggest the treatment is cost-effective
You can think of CEAC as a visual summary of the uncertainty produced by bootstrapping.
🏥 Clinical Example
Researchers compare online CBT vs in-person CBT for depression.
Using bootstrapping, they simulate thousands of possible cost-and-effect outcomes from the study data. These simulations show that online CBT is cost-effective in 70% of simulations at $30,000 per QALY, which is then plotted on the CEAC.
🧒 Explain it to a 10-year-old
Imagine you want to know how often a coin lands on heads, but you only flipped it 10 times.
Bootstrapping is like pretending to repeat the experiment many more times using the same results, so you can estimate what might happen if you flipped the coin thousands of times.
🧠 Memory Hook
“Bootstrapping means ‘re-playing the study again and again’ so we can see how shaky the cost-effectiveness results might be.” 🎥📊
-
CAP 1: Intro to Evidence-Based Practice19
-
CAP 2: Intro to Data and Statistics18
-
CAP 2.2: Statistical Test Types (REVISE)23
-
CAP 2.3: Reliability & Validity (REVISE)21
-
CAP 2.4: P-values, Confidence Intervals3
-
CAP 2.5: Type 1 and 2 Errors, Power, Causation7
-
CAP 3: Bias19
-
CAP 4: Randomised Controlled Trials15
-
CAP 5: Case Control Studies15
-
CAP 6: Cohort Studies26
-
CAP 7: Diagnostic Studies25
-
CAP 8: Systematic Reviews & Meta-Analyses36
-
CAP 9: Economic Analyses17
-
CAP 10: Qualitative Research25
-
CAP 11: Network Meta-Analysis24
