How to Read a Health Economic Evaluation

By Kevin Fernando · Oct 1, 2026 · 8 min read

Health economic evaluations follow a standardized structure. Read them by checking the study design and perspective, the comparator and time horizon, the effectiveness data and its source, and the cost inputs and sensitivity analysis. This sequence lets you verify the methods are sound and spot where conclusions rest on weak assumptions.

A health economic evaluation answers a specific question: does the health benefit of a new treatment justify its cost? These studies appear in journal articles, health technology assessment reports, and policy briefs. They look technical, but they follow a predictable structure. Learning to read that structure, and knowing where to check for weakness, lets you assess the claim without needing a health economics degree.

This guide teaches you to navigate a published evaluation in the order that matters. You will verify what type of analysis this is and from whose perspective costs are measured, confirm the study actually answers the question you care about, assess whether the health benefits rest on solid evidence, and interpret the final cost-effectiveness ratio in context. The same four-step sequence works whether you are reading a study about a drug, a device, a diagnostic test, or a public health program.

What you'll be able to do

After reading a health economic evaluation using this method, you will extract the core claim the study makes, verify that the methods used to reach it are sound, and identify the most common sources of inflated or misleading results. You will know what type of economic comparison was performed, whose costs were counted, what treatment was compared against what alternative, how long the claimed benefits were assumed to last, where the effectiveness data came from, and whether the conclusion holds if key assumptions change. You will be able to judge whether the study's answer applies to your context and whether the evidence supporting it is strong or weak.

Locate the study design and perspective

Start with the methods section. Your first task is to identify two things: what type of economic evaluation this is, and from whose viewpoint the costs are being measured. These two facts determine what the study is comparing and whose resources are being counted. Get these wrong and you will misinterpret everything that follows.

Identify which type of evaluation you're reading

Economic evaluations come in distinct types, each producing a different output metric. Cost-effectiveness analysis, the most common method, compares alternatives using natural health outcomes, the measures healthcare professionals use when discussing the relevant condition. It answers questions like "Does this drug cost more or less per patient hospitalization prevented than the current standard?" Cost-utility analysis is similar but measures outcomes in quality-adjusted life years, or QALYs, which blend length of life with quality of life. Cost-benefit analysis converts all outcomes to monetary units, so it can compare treatments for different conditions on the same scale. Cost-minimization analysis assumes two treatments produce identical health outcomes and compares only their costs. The type matters because it tells you what metric the final number represents. If you see a ratio expressed as cost per hospitalization prevented, you are reading a cost-effectiveness analysis. If it is cost per QALY, you are reading cost-utility analysis. The output metric is your first signal of what comparison is being made.

Determine whose costs are included

The perspective determines which costs are counted. Direct medical costs include hospitalization, medication, emergency transport, and medical care. Some evaluations stop there. Others add direct patient costs, which include out-of-pocket payments for treatment, transportation to medical visits, and modifications to the home as a result of illness. Still others adopt a societal perspective and add indirect costs, productivity loss from missed work or reduced earning capacity. The broader the perspective, the larger the total cost and the less favorable the cost-effectiveness ratio appears. A study counting only direct medical costs will show a treatment as more cost-effective than the same study counting societal costs. Check the methods section for the stated perspective. If it is not explicit, look at which costs appear in the cost section. This tells you whose resources are being weighed against the health benefit.

Check the comparator and time horizon

The comparator is the alternative the new treatment is being measured against. It might be the current standard of care, a different drug, no treatment, or placebo. The time horizon is how long the study tracks costs and benefits. These two details determine whether the study answers the question you care about. If you want to know whether a new drug is cost-effective compared to an older drug, but the study compares it to placebo, the answer does not apply to your decision. If you want to know the long-term cost-effectiveness but the study uses a one-year time horizon, the conclusion may not hold over the period you care about.

Find the comparator in the methods section. It should be stated explicitly. Find the time horizon the same way. Check whether it matches the question you are trying to answer. If the study uses a short-term time horizon (5 years or less ) but the treatment is expected to provide benefits over decades, flag this as a potential weakness. Long-term benefits extrapolated from short-term data are a common source of inflated cost-effectiveness ratios.

Examine the effectiveness data and its source

The effectiveness data, the health benefits claimed for the treatment, is where most evaluations fail. A study can have perfect methods and transparent costs, but if the effectiveness numbers are weak or extrapolated from thin evidence, the conclusion is weak. Your task here is to assess how confident you should be that the claimed benefit is real.

Verify the clinical outcomes come from published trials or real-world data

Effectiveness can come from randomized controlled trials, observational studies, disease registries, electronic health records, or modeled estimates. Each has different strengths. Trials provide controlled evidence but may not reflect how the treatment works in routine practice. Real-world data reflects actual use but may be confounded by unmeasured factors. The key question is not the study type but your confidence that the observed effect was actually caused by the intervention. A well-designed observational study can be more credible than a small, poorly conducted trial. A modeled evaluation can synthesize evidence across multiple studies and extrapolate beyond the trial time horizon, but it rests on assumptions about how effects will evolve. Look for the source of the effectiveness data in the methods or results section. If it comes from a published trial, check the trial's size and quality. If it comes from a model, look for the assumptions underlying the extrapolation. If effectiveness is based on expert opinion or a single small study, the conclusion is fragile.

Look for the time horizon mismatch

A common weakness is claiming long-term benefits on the basis of short-term data. Suppose a study compares a new cancer drug to standard treatment over a five-year time horizon. The trial data covers two years. The study assumes the benefit observed in year two persists unchanged for three more years without any new data. That assumption drives the cost-effectiveness ratio. If the benefit actually fades or if side effects emerge later, the ratio will be worse than reported. Check the methods section for the time horizon. Check the results section for how long the effectiveness data actually covers. If there is a gap, the study has extrapolated beyond its evidence. This is not necessarily wrong, modeled evaluations do this routinely, but it is a source of uncertainty. The larger the gap and the more uncertain the extrapolation, the more skeptical you should be of the final number.

Review the cost inputs and sensitivity analysis

The final cost-effectiveness ratio is only as good as the cost and effectiveness numbers fed into it. Your task is to understand which assumptions drive the result and whether the conclusion survives if those assumptions change.

Trace where unit costs come from

Costs can come from actual prices paid by hospitals or insurers, published cost databases, or rough estimates. Actual prices are most credible. Published databases are acceptable if they are current and relevant to the setting where the study was conducted. Rough estimates are weakest. Look for a cost section or appendix that lists the unit costs used, the price of a hospital day, a drug dose, a physician visit. Check whether each cost has a source cited. If costs are taken from a published database, check whether that database is current and whether it reflects prices in the country or healthcare system you care about. If costs are estimated, look for the method used. A cost-effectiveness ratio based on outdated or irrelevant prices may mislead you about whether the treatment is cost-effective in your setting.

Assess the sensitivity analysis

Sensitivity analysis tests what happens to the cost-effectiveness ratio if key assumptions change. A one-way sensitivity analysis varies one assumption at a time, for example, it recalculates the ratio if the drug price is 20 percent higher or if the effectiveness is 10 percent lower. A two-way or probabilistic sensitivity analysis varies multiple assumptions together. Look for a sensitivity analysis section or table. It will show the cost-effectiveness ratio under different scenarios. If the ratio stays below the cost-effectiveness threshold across a wide range of assumptions, the conclusion is robust. If the ratio flips from favorable to unfavorable when a single assumption changes modestly, the conclusion is fragile and depends critically on that assumption being correct. Pay special attention to assumptions about long-term effectiveness, drug prices, and hospitalization rates, these often drive the final result. If the study does not include a sensitivity analysis, that is a red flag.

Interpret the cost-effectiveness ratio against the stated threshold

The final cost-effectiveness ratio is a number like "£24,000 per QALY" or "$85,000 per hospitalization prevented." By itself, this number is meaningless. It only becomes meaningful when compared to a threshold, a decision rule for what counts as acceptable value for money. Thresholds vary by country and by the healthcare system making the decision. The UK's NICE has maintained a threshold of £20,000-30,000 per QALY since the early 2000s, though this is set to increase to £25,000-35,000 from April 2026. The World Health Organization has suggested thresholds of 1 to 3 times a country's gross domestic product per capita. The US Institute for Clinical and Economic Review provides results at thresholds of $50,000, $100,000, $150,000, and $200,000 per QALY. Only 8 of 36 countries examined have explicit thresholds formally defined in legislation or health technology assessment guidelines. Check what threshold the study uses and whether it applies to your context. A treatment that is cost-effective by one country's threshold may not be by another's. If the study does not state a threshold, you will need to know the relevant threshold for your decision-making context before you can interpret the ratio.

Spot common weaknesses that undermine the conclusion

Several reviews of economic evaluations have found numerous methodological deficiencies and failures to follow published guidelines. The most common weaknesses are these: effectiveness extrapolated far beyond the trial data without transparent assumptions; costs based on outdated prices or prices from a different country; a perspective that counts only direct medical costs when broader costs are relevant to the decision; a comparator that does not reflect current practice; and no sensitivity analysis or a sensitivity analysis that shows the conclusion is fragile. A study with one of these weaknesses may still provide useful information, but you should discount the conclusion accordingly. If a study has multiple weaknesses, treat the result as a rough estimate rather than a precise answer. The goal of reading an economic evaluation is not to accept or reject the conclusion wholesale, but to understand what it rests on and whether that foundation is solid enough to inform your decision.

Do this automatically

Manually extracting the study design, perspective, comparator, time horizon, and effectiveness data from a health economic evaluation requires reading through multiple sections of the methods, carefully cross-referencing cost definitions, and verifying that the chosen metrics align with the research question, a process that demands sustained attention to technical detail across dozens of studies. Software can systematically parse these components, flag inconsistencies between stated perspective and included costs, and extract the key parameters needed to assess whether an evaluation's claims are supported by its methodology. Company Deep Dive pulls the statement, computes the figures described above, and traces every number back to the line in the filing it came from, across as many subjects as you need, with Mimir attaching a citation to every figure. It will not predict a trial's outcome or advise on anyone's treatment. Those decisions stay with clinicians and regulators, and deliberately so. Every plan includes it, including the free one.

Start your first report and run it on something you already know well.

This article is general information about healthcare and life-sciences topics. It is not medical advice, diagnosis, or treatment guidance, and it is not a substitute for a qualified professional. Any treatment decision should be discussed with a clinician.