Health Economic Evaluation Report Template (CHEERS 2022)

Health economic evaluation report: decision problem, HEAP, perspective, horizon, discounting, costs, QALYs, model, ICER, NMB, PSA, equity, CHEERS map

Ciencia e investigaciónCompletaCHEERS 2022 (28 items; BMJ 2022;376:e067975)economic evaluation mathActualizada 29 oct 2026

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Cómo usar: Use it when you report a cost-effectiveness, cost-utility or other economic evaluation, trial-based or model-based. Fill the decision problem, perspective and time horizon first, then costs and outcomes. The main pitfall is totals, ICER and net benefit that do not reconcile, or a reference case that does not match the decision maker's current guide.

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Health Economic Evaluation Report: [Intervention] for [condition]

Report template for a health economic evaluation: a comparison of the costs and consequences of two or more health interventions, whether based on a single study (for example alongside a trial) or on a decision-analytic model. The sections follow the 28 items of the Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022; Husereau et al., BMJ 2022;376:e067975 and Value Health 2022;25(1):3-9); item numbers appear in square brackets, for example [10]. Replace every [bracketed] value and every row marked Example. Thresholds, discount rates and reference cases differ by jurisdiction and change over time: section 8 lists dated examples to check. This is a document format, not financial, medical or reimbursement advice; check the current official methods guide of the decision maker and involve a qualified health economist.

Document control

Field	Value
Document ID	[HEE-ACRONYM-001]
Evaluation type	[Cost-utility / Cost-effectiveness / Cost-benefit / Cost-consequence / Cost-minimisation]
Study basis	[Trial-based / Model-based / Both]
Analysis plan reference	[HEAP version and registration or publication]
Model file and version	[File name, software, version, checksum]
Decision maker or audience	[Agency, payer, guideline group, journal]
Version	[0.1]
Status	[Draft / Internal QA / External review / Approved]
Owner	[@lead-health-economist]
Approvers / reviewers	[@clinical-lead], [@statistician], [@model-validator]
Last updated	YYYY-MM-DD
Classification	[Confidential / Public]

Approvals

Role	Name	Decision	Date
Lead health economist	[@name]	[Approved]	YYYY-MM-DD
Independent model validator	[@name]	[Model checks passed]	YYYY-MM-DD
Clinical lead	[@name]	[Clinical assumptions approved]	YYYY-MM-DD

Revision history

Version	Date	Author	Change summary
0.1	YYYY-MM-DD	[@name]	First draft of base case
0.2	YYYY-MM-DD	[@name]	Example: Hospitalisation costs updated to the latest national tariff; price year moved to 2025
1.0	YYYY-MM-DD	[@name]	Approved after independent model validation

Title and abstract
Introduction
Methods
Results
Discussion
Other relevant information
Model validation and quality assurance
Jurisdiction reference cases (dated examples)
CHEERS 2022 traceability
Glossary and references

1. Title and abstract

The title and abstract identify the evaluation and summarise it.

1.1 Title [1]

[Type of economic evaluation] of [intervention] versus [comparator] for [population] in [setting or country]

1.2 Abstract [2]

Heading	Content
Context and objective	[Decision problem in one or two sentences]
Methods	[Evaluation type, perspective, time horizon, model or trial, discount rate, price year]
Results	Example: Remote monitoring cost £2,360 more per patient and gained 0.094 QALYs over a lifetime horizon, an incremental cost-effectiveness ratio of £25,106 per QALY gained. The probability of being cost-effective was 0.49 at £25,000 and 0.66 at £35,000 per QALY.
Alternative analyses	[Key scenario and subgroup results]
Conclusions	[Interpretation with reference to the decision threshold and uncertainty]

2. Introduction

This section states the decision problem [3].

Item	Content
Background	[Burden of the condition, current practice, the new intervention]
Decision problem	Example: Should a national health service fund nurse-led remote monitoring for adults discharged after a heart failure admission?
Policy relevance	[Who decides, when, and what they need]
Study question	[Cost-effectiveness of X versus Y in population P, from perspective Q, over horizon T]
Previous economic evidence	[Summary of existing evaluations and why a new one is needed]

3. Methods

This section reports the design of the evaluation, items [4] to [21].

3.1 Health economic analysis plan [4]

Item	Detail
Plan exists?	[Yes / No]
Where available	[Registry, repository or publication]
Date finalised	YYYY-MM-DD, [before or after outcome data were available]
Departures from the plan	[List with reasons, or "none"]

3.2 Study population [5]

Item	Detail
Population	Example: Adults aged 50 and over discharged after a first hospital admission for heart failure with reduced ejection fraction
Baseline characteristics used in the model	[Mean age, sex mix, severity]
Subgroups	[Pre-specified subgroups and why they matter]
Source	[Trial, cohort or registry used to describe the population]

3.3 Setting and location [6]

[Country, health system, care setting and other context that may affect costs, care pathways and outcomes.]

3.4 Comparators [7]

ID	Comparator	Description	Why chosen
CMP-01	Example: Nurse-led remote monitoring	Daily weight and symptom reports via a tablet, reviewed by a specialist nurse, 12 months	New intervention
CMP-02	Example: Usual care	Routine follow-up by primary care and a cardiology clinic	Current standard in the setting
CMP-03	[Other relevant option]	[Description]	[Include or justify exclusion]

3.5 Perspective, time horizon and discounting [8, 9, 10]

Item	Choice	Justification
Perspective [8]	Example: Health service and personal social services	Matches the decision maker's reference case
Costs included	[Categories within the perspective]	[Reason]
Time horizon [9]	Example: Lifetime (to age 100)	Heart failure affects survival, so effects extend beyond the trial
Cycle length (models)	[3 months]	[Matches the timing of events]
Discount rate [10]	Example: 3.5% per year for costs and outcomes	[Reference case; scenario at 1.5% for outcomes]

Costs and outcomes in year $t$ are discounted to present value with rate $r$ :

PV = \sum_{t=0}^{T} \frac{X_t}{(1+r)^{t}}

Example: A cost of £1,000 in each of years 0 to 4, discounted at 3.5%, has a present value of £4,673.08.

Year	Discount factor at 3.5%	Present value of £1,000
0	1.0000	£1,000.00
1	0.9662	£966.18
2	0.9335	£933.51
3	0.9019	£901.94
4	0.8714	£871.44
Total		£4,673.08

3.6 Outcomes: selection, measurement and valuation [11, 12, 13]

Item	Detail
Outcome measure selected [11]	Example: Quality-adjusted life years (QALYs); secondary: hospital admissions avoided, life years
Why this measure	[Decision maker's reference case; captures length and quality of life]
Measurement [12]	[Instrument, for example a generic preference-based questionnaire, administered at stated time points in the trial]
Valuation [13]	[Value set or tariff, its country and year, population whose preferences were used]
Adjustments	[Age-related utility decrements, disutilities for events]

3.7 Measurement and valuation of resources and costs [14]

Resource	Quantity source	Unit cost source	Unit cost (Example)
Remote monitoring kit and platform	Supplier quote	Supplier price	£620 per patient-year
Specialist nurse time	Trial time log	National pay scales with on-costs	£58 per hour
Heart failure hospital admission	Trial and registry	National tariff	£3,950 per admission
Primary care consultation	Trial diaries	National unit costs	£42 per visit
[Resource]	[Source]	[Source]	[Value]

3.8 Currency, price date and conversion [15]

Item	Detail
Currency	Example: Pounds sterling (GBP)
Price year	Example: 2025
Inflation index	[Index name and source]
Currency conversion	[Purchasing power parity rate, source and year, if costs from other countries were used]

3.9 Rationale and description of the model [16]

[Describe the model type, structure, health states or pathways and why this structure suits the decision problem. Make the model available or explain how to access it.]

Example: A Markov cohort model with three health states and a 3-month cycle. Patients start in "Stable heart failure"; each cycle they may be admitted to hospital, which raises the risk of death, or die. Remote monitoring is assumed to reduce the rate of admission.

3.10 Analytics and assumptions [17]

ID	Assumption or method	Justification	Tested in
ASM-01 (Example)	Remote monitoring reduces admissions by a constant relative rate for 12 months, then the effect stops	Duration of service; no evidence of lasting effect	Scenario SC-02 (effect lasts 3 years)
ASM-02	[Extrapolation of survival beyond trial follow-up, model chosen]	[Fit statistics, clinical plausibility]	[Scenario]
ASM-03	[Handling of missing cost or utility data]	[Method, for example multiple imputation]	[Scenario]
ASM-04	Half-cycle correction	[Method]	Not applicable

3.11 Characterising heterogeneity [18]

[Describe methods for estimating how results vary between subgroups defined by baseline characteristics, for example age or disease severity, and say which subgroups were pre-specified.]

3.12 Characterising distributional effects [19]

[Describe how impacts are distributed across groups that are relevant for equity, for example by socioeconomic status or region, and any method used, such as distributional cost-effectiveness analysis or equity weights. If not done, say so and why.]

3.13 Characterising uncertainty [20]

Type	Method
Parameter uncertainty	Example: Probabilistic sensitivity analysis, 5,000 Monte Carlo draws; beta distributions for probabilities and utilities, gamma for costs, log-normal for relative effects
Deterministic sensitivity	One-way analyses on each key parameter across its 95% CI or a plausible range
Structural uncertainty	Scenarios on model structure and assumptions (ASM IDs)
Methodological uncertainty	Scenarios on discount rate and perspective
Value of information	[Expected value of perfect information, if done]

3.14 Approach to engagement with patients and others [21]

[Describe how patients, carers, the public, clinicians or payers were involved in the design, for example in choosing outcomes or reviewing assumptions. If no engagement took place, state that.]

4. Results

This section reports items [22] to [25].

4.1 Study parameters [22]

ID	Parameter	Base case	Distribution	Source
PAR-01	Example: Quarterly probability of admission, usual care	0.080	Beta	Fictional registry
PAR-02	Rate ratio for admission, remote monitoring	0.80	Log-normal	Fictional trial
PAR-03	Utility, stable heart failure	0.72	Beta	Trial questionnaire data
PAR-04	Disutility of an admission	0.10 for one cycle	Beta	Literature
PAR-05	Cost per admission	£3,950	Gamma	National tariff
PAR-06	[Parameter]	[Value]	[Distribution]	[Source]

4.2 Summary of main results [23]

Disaggregated mean costs per patient, discounted (Example):

Cost category	Remote monitoring	Usual care	Difference
Intervention delivery	£2,150	£0	£2,150
Hospital admissions	£8,940	£9,980	-£1,040
Primary care	£2,310	£2,060	£250
Medicines	£1,420	£420	£1,000
Total	£14,820	£12,460	£2,360

Base-case cost-effectiveness (Example):

Comparator	Total cost	Total QALYs	Incremental cost	Incremental QALYs	ICER (£ per QALY)
Usual care	£12,460	4.118
Remote monitoring	£14,820	4.212	£2,360	0.094	25,106

The incremental cost-effectiveness ratio (ICER) and the net monetary benefit (NMB) at a threshold $\lambda$ per QALY are:

\text{ICER} = \frac{\Delta C}{\Delta E}, \qquad \text{NMB} = \lambda\,\Delta E - \Delta C

Example: 2,360 / 0.094 = 25,106. Net monetary benefit is positive when the ICER is below the threshold:

Threshold (£ per QALY)	Incremental NMB
20,000	-£480
25,000	-£10
30,000	£460
35,000	£930

4.3 Effect of uncertainty [24]

Deterministic one-way sensitivity analysis (Example):

Parameter varied	Low value	ICER at low	High value	ICER at high
Intervention delivery cost	£1,720 (-20%)	£20,532	£2,580 (+20%)	£29,681
[Rate ratio for admission]	[0.70]	[value]	[0.90]	[value]
[Utility, stable]	[value]	[value]	[value]	[value]

The intervention cost row changes only the intervention delivery cost, so the incremental cost moves by £430 either way (£1,930 and £2,790) while incremental QALYs stay 0.094.

Probabilistic sensitivity analysis (Example):

Threshold (£ per QALY)	Probability remote monitoring is cost-effective
20,000	0.38
25,000	0.49
30,000	0.58
35,000	0.66

Present the cost-effectiveness plane (scatter of incremental cost against incremental QALYs for every draw) and the cost-effectiveness acceptability curve as figures, and report scenario results:

ID	Scenario	ICER (£ per QALY)
SC-01	Discount rate for outcomes 1.5%	[value]
SC-02	Effect of monitoring lasts 3 years (ASM-01)	[value]
SC-03	Societal perspective including carer time	[value]

4.4 Effect of engagement with patients and others [25]

[Report any difference that engagement made to the approach or the findings, for example an outcome added at the request of a patient panel. If there was no engagement, state that.]

5. Discussion

This section covers item [26].

5.1 Key findings

[Summarise the base case and the main uncertainty in plain language.]

Example: The point estimate of £25,106 per QALY lies inside a threshold range of £25,000 to £35,000, but the probability of cost effectiveness at the lower end of that range is about one half, so the decision is sensitive to the threshold chosen and to the cost of the service.

5.2 Limitations

[Evidence gaps, for example a short trial follow-up extrapolated to a lifetime]
[Structural assumptions with large effects on the ICER]
[Costs or outcomes outside the perspective that may matter]

5.3 Generalisability and current knowledge

[Discuss how the results transfer to other settings, how they compare with previous evaluations and what new evidence would most reduce decision uncertainty.]

5.4 Equity and ethical considerations

[Summarise the distributional findings from section 3.12 and any ethical issues for the decision maker.]

6. Other relevant information

This section covers items [27] and [28].

Item	Detail
Source of funding [27]	[Funder, grant number, role in identification, design, conduct and reporting]
Conflicts of interest [28]	[Each author's interests, following the journal or ICMJE disclosure form]
Model and data availability	[Repository, licence, conditions]

7. Model validation and quality assurance

Record the checks performed on the model and the analysis.

ID	Check	Method	Done by	Result
QA-01	Face validity	Clinical experts review structure and assumptions	[@name]	[Pass / issues]
QA-02	Technical verification	Extreme value tests, cohort traces sum to 1, double programming of key formulas	[@model-validator]	[Pass]
QA-03	Internal validity	Model reproduces trial results it was fitted to	[@name]	[Pass]
QA-04	External validity	Model predictions compared with independent data	[@name]	[Pass]
QA-05	Cross-validity	Results compared with other published models	[@name]	[Pass]

8. Jurisdiction reference cases (dated examples)

Methods guides and thresholds change. Check the current official page before relying on any figure below.

Jurisdiction	Methods guide	Points to check	Status checked
England (example)	NICE health technology evaluations manual (PMG36), https://www.nice.org.uk/process/pmg36	Perspective, discount rate, outcome measure, threshold. NICE moved its standard threshold range from £20,000 to £30,000 per QALY to £25,000 to £35,000 per QALY from April 2026, as reported by the Office of Health Economics on 2026-02-25; confirm on the current NICE manual	October 2026; NICE pages blocked automated access, figure confirmed through OHE, 2026-02-25
United States (example)	Second Panel on Cost-Effectiveness in Health and Medicine (Sanders et al., JAMA 2016;316(10):1093-1103)	Healthcare sector and societal reference cases, impact inventory, discount rate	October 2026
European Union (example)	Regulation (EU) 2021/2282 on health technology assessment: joint clinical assessments address clinical aspects, while economic evaluation and pricing remain national decisions	Which national agency and guide applies; check the Commission HTA pages	October 2026; official pages blocked automated access, check the current text
[Your jurisdiction]	[Guide and URL]	[Items]	YYYY-MM-DD

9. CHEERS 2022 traceability

Item	Topic (short)	Section in this file	Page
1	Title	1.1	[p]
2	Abstract	1.2	[p]
3	Background and objectives	2	[p]
4	Health economic analysis plan	3.1	[p]
5	Study population	3.2	[p]
6	Setting and location	3.3	[p]
7	Comparators	3.4	[p]
8	Perspective	3.5	[p]
9	Time horizon	3.5	[p]
10	Discount rate	3.5	[p]
11	Selection of outcomes	3.6	[p]
12	Measurement of outcomes	3.6	[p]
13	Valuation of outcomes	3.6	[p]
14	Measurement and valuation of resources and costs	3.7	[p]
15	Currency, price date and conversion	3.8	[p]
16	Rationale and description of model	3.9	[p]
17	Analytics and assumptions	3.10	[p]
18	Characterising heterogeneity	3.11	[p]
19	Characterising distributional effects	3.12	[p]
20	Characterising uncertainty	3.13	[p]
21	Approach to engagement with patients and others	3.14	[p]
22	Study parameters	4.1	[p]
23	Summary of main results	4.2	[p]
24	Effect of uncertainty	4.3	[p]
25	Effect of engagement with patients and others	4.4	[p]
26	Study findings, limitations, generalisability and current knowledge	5	[p]
27	Source of funding	6	[p]
28	Conflicts of interest	6	[p]

10. Glossary and references

10.1 Glossary

Term	Meaning in this template
QALY	Quality-adjusted life year: a year of life weighted by a utility between 0 (dead) and 1 (full health)
ICER	Incremental cost-effectiveness ratio: extra cost divided by extra health effect
NMB	Net monetary benefit: health gain valued at the threshold minus the extra cost
Threshold	The value per unit of health gain the decision maker uses to judge cost effectiveness
Perspective	Whose costs and outcomes are counted (health service, payer, society)
PSA	Probabilistic sensitivity analysis: repeated model runs with inputs drawn from their distributions
CEAC	Cost-effectiveness acceptability curve: probability of cost effectiveness across thresholds
HEAP	Health economic analysis plan

10.2 Methodological references

Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMJ. 2022;376:e067975. https://doi.org/10.1136/bmj-2021-067975 (also Value Health. 2022;25(1):3-9, https://doi.org/10.1016/j.jval.2021.11.1351)
Elvidge J, Hawksworth C, Avşar TS, Zemplenyi A, et al. Consolidated Health Economic Evaluation Reporting Standards for interventions that use artificial intelligence (CHEERS-AI). Value Health. 2024;27(9):1196-1205. https://doi.org/10.1016/j.jval.2024.05.006
Sanders GD, Neumann PJ, Basu A, Brock DW, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA. 2016;316(10):1093-1103. https://doi.org/10.1001/jama.2016.12195

For interventions that use artificial intelligence, also report the CHEERS-AI items. Related templates in this library: science/clinical-trial-report (the trial behind a trial-based evaluation), science/trial-protocol (planning the trial and its economic outcomes), academic/stats-analysis-plan (analysis plan structure) and academic/stats-confidence-intervals (precision of estimates).

Standard and licence note

Structure aligned with the CHEERS 2022 statement (Husereau et al., BMJ 2022;376:e067975 and Value Health 2022;25(1):3-9; 28 items), published open access and listed by the EQUATOR Network. This template follows the CHEERS 2022 item numbering with short topic names and original wording; it reproduces no checklist or explanation text. Official checklist: CHEERS Task Force via the EQUATOR Network, https://www.equator-network.org/reporting-guidelines/cheers/. Checked in October 2026; check the current version, any extension that applies and the current methods guide of your decision maker before use. This is a document format, not financial, medical or reimbursement advice; consult a qualified health economist. Template licence: MIT.

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