Guideline for Preparing
Cost-Effectiveness Evaluation
to the Central Social Insurance
Medical Council—
Version 3.0 approved by CSIMC on 19th January, 2022

The English version is a translation of the original version in Japanese. The Japanese version is preferentially applied in cases of discrepancy between the two versions.

Center for Outcomes Research and Economic Evaluation for Health, National Institute of Public Health (C2H)

Contents

1

Objectives─ ──────────────────────────── 42Analysis perspective───────────────────────── 5

3Target population ────────────────────────── 6

4Comparator───────────────────────────── 7

5Additional benefits─ ──────────────────────── 8

6Methods of analysis─ ──────────────────────── 11

7Time horizon─ ─────────────────────────── 12

8

Choice of outcome measure─ ──────────────────── 13

9

Sources of clinical data (excluding costs)──────────────── 15

10

Calculation of healthcare costs─ ─────────────────── 16

11

Public long-term care costs and productivity loss ──────────── 19

12

Discounting───────────────────────────── 21

13

Modeling────────────────────────────── 22

14

Uncertainty ───────────────────────────── 23

Terminology ─ ───────────────────────────── 24
Abbreviations ────────────────────────────── 29

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1

Objectives
1.1

This guideline presents standard methods to perform cost-effectiveness evaluations of medicines and medical devices selected by the Central Social Insurance Medical Council (“selected
technologies”).

1.2This guideline is applied to manufacturers’ submissions and academic analysis (review andre-analysis).

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2

Analysis perspective
2.1

The perspective of the analysis should be specified. In particular, the analysis should consider therange of costs corresponding to this perspective.

2.2“Public healthcare payer’s perspective” is a standard. It uses costs, comparator, and target populations to reflect the situation of public healthcare insurance in Japan.

2.2.1

Even when an analysis is conducted from a perspective other than the “public healthcare
payer’s perspective,” an analysis from the “public healthcare payer’s perspective” should
be submitted.

2.2.2

There are some healthcare technologies that are not covered by public healthcare insurance but are publicly funded, such as some prophylactic procedures (e.g., health checkups, vaccinations). Analyses including these technologies should be submitted from the“public healthcare payer’s perspective.”

2.3

If the effect on public long-term care costs is important with regard to the selected technology,
it is acceptable to perform an analysis from the “public healthcare and long-term care payer’s
perspective.”

2.4

If the introduction of a selected technology influences productivity, it is acceptable to perform an
analysis that includes productivity loss from the broader perspective.

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3

Target population
3.1

Patient populations that meet the indications when the target technology is selected should beconsidered as the target population of the cost-effectiveness evaluation.

3.1.1

In the case that a new indication is approved between the selection of target technology
and the determination of the framework of analysis, it is to be included in the target
population.

3.1.2

A new indication is added after the time defined by item 3.1.1 and a new evaluation is
performed after the first evaluation is completed if it may influence the results.

3.2

An analysis should be conducted for each population if the technology has multiple indications or subpopulations that differ in outcome, application method/dose, and administration orcomparator.

3.2.1

However, if item 3.2 is difficult to achieve, it is acceptable to perform analyses of limited
population(s) considering factors such as the number of patients or features of the illness.
The exemption is determined based on agreement between the manufacturer and the
National Institute of Public Health/public academic group in consultation.

3.3

The percentage of each patient group should be determined in a manner that reflects the recent
clinical context of the target population.

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4Comparator
4.1

The comparator should be principally selected from technologies that are widely used in clinicalpractice and are expected to be replaced by the selected technology when it is introduced totreat the target population. Among them, technologies that result in better outcomes should beselected.

4.1.1

Non-treatment or watchful waiting can also be used as comparators.

4.1.2

Except for the cases described in item “4.1.1,” the comparator should be selected fromtechnologies that can be used by public healthcare insurance.

4.1.3

If a single comparator cannot be determined based on item “4.1,” the comparator should
be selected by considering the comparators in randomized controlled trials (RCTs), similar technologies for the official pricing, and cost-effectiveness based on agreement after
consultation with C2H.

4.2

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Sufficient explanation of the reasons underlying the selection of the comparator is needed.

5

Additional benefits
5.1

When a cost-effectiveness evaluation is conducted, whether the additional benefit of the selected
technology to the comparator is proven should first be evaluated.

5.2

Evaluations of the additional benefit should be conducted on the basis of a systematic review(SR) of RCTs compared with a technology selected in section “4.” If appropriate, the results of
unpublished clinical studies/trials may also be included in the SR.

5.2.1

When an SR is conducted, research questions (RQs) should be clearly presented. For
example, a definition of structured RQs according to PICO (P: patient, I: intervention, C:
comparator, O: outcome) may be provided.

5.2.2

There may be technologies with a similar action mechanism or function category to theselected technologies or comparator(s) determined in section “4” that are expected toshow equivalent outcomes. These technologies can be included as an intervention (I) or
comparator (C) in the SR if deemed appropriate after consultation with C2H.

5.2.3

For outcome (O) in item “5.2.1,” the most appropriate clinical outcomes (e.g., a “true outcome”) should be selected from among clinical effectiveness, safety, and health-related
quality of life (HRQOL).

5.2.4

Descriptions of the inclusion/exclusion criteria, databases used, search algorithm, andresearch selection process (inclusion flow diagram) are required in accordance with the
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement.

5.2.5

It is acceptable to utilize any existing reliable SR. In such cases, the existing review will be
used solely or in combination with a new additional study. In this case, it should confirm
the consistency of the existing review by considering the RQs and coverage of the mostrecent literature.

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5.2.6

If appropriate, pooled results via a meta-analysis should be presented. In such cases, therequired reporting factors include the statistical method, assessment of heterogeneity,
forest plot, pooled results, and confidence interval.

5.2.7

When it is obvious that no RCTs have been performed, the process described in section“5.2” can be skipped upon agreement in consultation.

5.2.8

A time point between determining the framework of analysis and manufacturer’s submission can be used as a cut-off date for the literature search in the SR.

5.2.9

There may be cases in which the results of new clinical trials are published after the cut-off
date defined in item “5.2.8” but are regarded as important information for the cost-effectiveness evaluation (e.g., clinical trials with a large sample size or reliable results that differ
from current studies). Inclusion of these trials in the SR should be considered. In that case,additional SR is not required.

5.3

When no studies are available based on the result of SR described in section “5.2,” the additional
benefit is evaluated by SR of comparative non-RCT (e.g., observational) studies based on section “5.2.” In that case, sufficient explanation regarding the research quality is needed (e.g., study
design, differences in background factors between groups, statistical analysis, sample size, andnumber of institutions).

5.4

When more reliable results are obtained, the additional benefit can be evaluated via re-analysis ofexisting observational study or registry data, if agreed upon in consultation. In that case, sufficient
explanation on the research quality is needed (e.g., study design, differences in background factors between groups, methods of statistical analysis, sample size, and number of institutions).

5.5

When there are no RCT studies using the same comparator selected in section “4” but there are
RCT studies of the selected technology compared to others, the additional benefit is evaluated via
an indirect comparison using the SR results.

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5.6

When only single-arm clinical studies of selected technologies are available, an indirect comparison should be performed based on the SR results of the selected technologies and the comparator.

5.7

When an indirect comparison is performed, the following items should be considered.

5.7.1

When individual patient data are available, the difference of background factors should
be adjusted using an appropriate method such as MAIC (matched adjusted indirectcomparison).

5.7.2

When individual patient data are not available, an adjusted indirect comparison using
RCT or network meta-analysis should be used.

5.7.3

When neither individual patient data nor results of RCTs are available, a naïve indirectcomparison may be acceptable if other methods cannot be used. In such a case, theuncertainty of the results should be carefully considered.

5.7.4

If an indirect comparison is conducted, sufficient explanation on the prerequisites for theindirect comparison (e.g., heterogeneity of illness, severity, and patient background or
similarity of the studies) is also needed.

5.8

There may be cases in which the results obtained by the methods in items “5.3” to “5.7” haveserious problems regarding the quality of the studies. However, it is expected that the selected
technology is not inferior to the comparator. In such cases, the analysis described in section “6”can be performed, assuming the outcome of the selected technology is equivalent to that of thecomparator.

5.9

When there are no clinical data available on the selected technology in humans, the analysis
described in section “6” can be performed, assuming the outcome of the selected technology isequivalent to that of the comparator(s), if appropriate. This is based on considering the approvalof the Pharmaceuticals and Medical Devices Agency (PMDA).

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5.10

When the SR results obtained by the methods in items “5.2” to “5.7” show that the outcomes ofthe selected technology are inferior to those of the comparator(s), no cost-effectiveness analysis isperformed.

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6

Methods of analysis
6.1

A cost-effectiveness analysis should be used. In this guideline, cost-effectiveness analysis is defined
as an analysis that calculates cost and effectiveness separately without converting effectivenessinto monetary units.

6.2

If the analysis described in section “5” reveals an additional benefit, the incremental cost-effectiveness ratio (ICER) should be calculated based on the expected cost and effectiveness in eachtreatment group.

6.3

In the following cases, only the expected cost and effectiveness in each group need to be presented and the ICER should not be calculated.

6.3.1

In cases where the technology has better effectiveness and lower costs relative to thecomparator, the technology is considered “dominant” without a calculation of ICER.

6.3.2

A cost comparison with the comparator (a so-called “cost minimization analysis” [CMA])
should be performed if the analysis described in section “5” does not demonstrate an
additional benefit but the outcome of the selected technology appears to be equivalent
to that of the comparator. In such a case, the result is either “cost saving” or “cost increase.”

6.4

If the selected technology has multiple indications or sub-populations defined in item “3.2” and/or “3.2.1,” ICER should be calculated for each indication or sub-population.

6.5

If a cost-effectiveness analysis for a selected technology published in an academic journal or an
evaluation from a health technology assessment (HTA) agency is available, these results should
also be presented.

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7Time horizon
7.1

The time horizon should be sufficiently long to evaluate the influence of the technology on costand effectiveness.

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7.2

The same time horizon should be applied for both cost and effectiveness.

7.3

The reason for setting this time horizon should be specified.

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Choice of outcome measure
8.1

Quality-adjusted life years (QALY) should be used in principle.

8.1.1

When it is difficult to calculate QALY, and CMA is applied, other outcome measures canbe used, if appropriate.

8.2

When QALY is calculated, the QOL score should be measured by a preference-based measure(PBM) in principal.

8.2.1

If Japanese QOL scores (utilities) are newly collected for a cost-effectiveness analysis,
EQ-5D-5L is recommended as the first choice.

8.2.2

If data corresponding to item “8.2” are unavailable, it is acceptable to use mapping ofother appropriate HRQOL data. When using a QOL score obtained from mapping, theconversion into a QOL score via an appropriate method should be explained.

8.3

When the QOL score is assessed by PBM, the subjects’ own QOL responses should be used.

8.3.1

In the case of using PBMs, responses from a proxy (e.g., family member or caregiver) may
be used only when the subject cannot respond.

8.3.2

In the case of using PBMs, proxy responses from a healthcare professional should be
avoided due to possible discrepancies with subjects’ own responses.

8.3.3

If it is difficult to directly collect QOL scores from patients, it is acceptable for general
people to evaluate the presented health scenario by standard gamble (SG), time trade-off(TTO), and discrete choice experiment (DCE). However, QOL scores measured via thesemethods are largely influenced by the presented health scenario. Therefore, the limitations of this method should be considered; for example, the same QOL score should be
used for the same health state.

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8.4

As long as a QOL score that satisfies items “8.2” and “8.3” is available, the use of Japanese results
is preferentially recommended.

8.4.1

If Japanese research is absent or insufficient but high-quality research is available overseas, it is acceptable to use the data collected overseas.

8.5

In the case of the analysis from the public healthcare and long-term care payer’s perspective, the
QOL scores’ influence on the informal caregiver may be considered if actual data exist.

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9Sources of clinical data (excluding costs)
9.1

ICER calculations should preferentially use effectiveness, safety, and QOL data (inclusion of parameters such as transition probability for model analysis) derived from high-quality research with ahigh evidence level reflective of practical clinical results in Japan.

9.1.1

The selection of effectiveness, safety, and QOL data on the basis of an SR of all the clinicalresearch is recommended. This review may also include unpublished clinical study/trial
data if appropriate.

9.1.2

Data with a high evidence level should be used preferentially. The use of data deemed
appropriate from the viewpoints of research quality, target population, and external
validity is recommended (for example, it is possible that the results of an RCT may differ
markedly from practical clinical results).

9.1.3

Data derived from re-analysis of existing study and/or registry data can be used if appropriate. In this case, detailed information on patient background, statistical methods, etc.
must be provided.

9.2

Japanese data should be used preferentially if there is evident heterogeneity between Japaneseand overseas data.

9.3

If the data do not differ statistically significantly between the selected technology and the comparator(s), pooled data of both groups should be applied. Otherwise, when considering factors
such as data or rationale that support the difference, effect size (clinical meaning), it should be
explained that the difference is interpretable.

9.4

Regarding evaluation of medical devices, if there are reliable and quantitative data, analysis
reflecting “learning effect” (i.e., improvement of treatment effect via the accumulation of clinicians’
experience) or “product improvement effect” can be submitted in addition to analyses not considering the effects, upon agreement in consultation with C2H.

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10 Calculation of healthcare costs
10.1

Only public healthcare costs should be included in the case of analysis from public healthcare
payers’ perspective.

10.2

The healthcare costs of each health state include only related costs that are directly affected by theselected technology. and do not include unrelated costs.

10.3

The healthcare costs of each health state should reflect the average resource consumption andstandard clinical practices in Japan.

10.4

It is recommended that claims databases established in Japan, which reflect actual clinical practice from the viewpoint of item “10.3,” should be used to estimate the costs of each health state
if appropriate. However, this recommendation does not apply to cases in which it is difficult todefine health states using only information from claims data or if there are insufficient data in the
database.

10.4.1

A definition of each health state and its rationale is required when claims data are used
for cost estimation.

10.4.2The methods and rationale for the method to estimate costs (including handling outliers
and unrelated costs) should be shown.

10.5

Micro-costing (by medical fee schedule etc.) based on the definitions of the standard clinical process can be used if it is difficult to estimate the costs of each health state via a claims database orif micro-costing is more appropriate.

10.5.1In the case of the application of micro-costing, the rationale for costing should be shown
from the viewpoint of item “10.2.” It may be better to identify relevant items and/or estimate the amount of medical resource consumption in the claims database.

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10.5.2

When micro-costing is used, the medical resource consumption and unit costs should bereported separately.

10.5.3

In principle, for the estimation of resource consumption in item “10.5.2,” the resource
consumption of injection products should be calculated by the number of vials rather
than by patient dosage.

10.6

The estimation should include not only the costs of the selected technology and the comparator(s)but also the costs of adverse events and related future events etc.

10.7

An analysis of public healthcare costs should include not only the portion of costs paid by the
insurer but also those paid by the government and patients as co-payment (i.e., the total publichealthcare expense).

10.7.1

Based on the principal in item “2.2.2,” the analysis should include the costs of healthcheck-ups, vaccinations, or similar procedures that are funded publicly but not reimbursed by Japan’s public healthcare insurance.

10.8

Unit costs should be derived from the latest medical fee schedule, drug price lists, or similar
resources. It is particularly essential to use the latest unit costs for the selected technology orcomparator(s).

10.8.1

Even if existing cost-of-illness studies or analyses of claims data are used, unit costs atthe time of evaluation rather than when the medical resources are consumed should
be applied. It is acceptable to make adjustments such as multiplication by the medical
payment system revision rate.

10.8.2

Such adjustments may be omitted if the influence on results is minimal.

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10.9

If generics of the comparator(s) are already on the market, an analysis using these costs should ]also be submitted.also submitted.

10.10 Even if the costs of selected technology and/or comparator(s) are included in a bundled payment,
the estimation should be based on fee-for-service payment.

10.11 Future costs should also be estimated based on current medical resource consumption and unit
costs.

10.12 Calculations of medical resource consumption based on other countries’ data will require attention to possible differences in healthcare technology use between Japan and other countries. The
unit costs in Japan should be applied in the analysis.

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Public long-term care costs and productivity loss
11.1

An analysis from the perspective of the public healthcare and long-term care payer may include
public long-term care costs in addition to base-case analysis. It is acceptable to include public
long-term care costs in additional analyses only if they can be estimated by Japanese data.

11.2

When public long-term care costs are included in the analysis, it is recommended that these costs
be calculated based on the care level.

11.3

The amount utilized under public long-term care insurance should be based on the actual
resource consumption. If this consumption is difficult to determine, it is acceptable to use theaverage amount utilized per beneficiary or similar data.

11.4

An analysis including productivity loss can be additionally performed on top of the base-case analysis. However, judgments regarding the appropriateness of including productivity losses should
consider the possibility of working in the context of the illness characteristics. It is acceptable toinclude productivity losses in additional analyses only if they can be estimated by Japanese data.

11.5

Decreases in productivity losses may be classified as follows:		(A) those arising directly from healthcare technology (e.g., treatment-related shortening of

hospital stay) and		(B) those arising indirectly from outcome improvements (e.g., alleviation of illness, survival

period extension).
When productivity loss is included in an analysis, only (A) should be included in the cost calculation.

11.6

Productivity losses should be estimated using the human capital method. This method estimates
the loss via the expected earned wage in the absence of illness.

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11.6.1

The unit wage used for estimations of productivity loss should be the average wage
across all industries, all ages, and both genders or the average wage for each age groupin all industries and both genders derived from the latest “Basic Survey on Wage Structure” (Wage Census) and not discriminate by income.

11.6.2Estimations of productivity loss require an actual investigation into the employment
status in the target population (i.e., a measure of the days or hours of work missed). The
actual measured number of days or hours should then be multiplied by the average wage
across all industries, all ages, and both genders to accurately estimate the productivity
loss.

11.6.3

If the item described in item “11.6.2” is difficult to perform, productivity loss should be
calculated by multiplying the expected number of days (excluding holidays) or hours
of work missed multiplied by the average wage across all industries, all ages, and bothgenders. A 100% employment rate should be assumed for those aged 18 years and older.
However, note that this method may overestimate productivity losses.

11.7

If other individuals (e.g., family members) experience productivity losses due to the provisionof nursing or informal patient care, it is acceptable to count these productivity losses as costs
under the same conditions and using the same methods as those used to calculate the patient’s
productivity loss.

11.7

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Time costs that are unrelated to a decrease in work should not be included in the cost estimations.

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Discounting
12.1

Future costs and effectiveness must be discounted and converted into present values.

12.1.1

Discounting is not needed if the time horizon is one year or less or is otherwise sufficiently
short to ignore the influence of discounting.

12.2

Both cost and effectiveness should be discounted at a rate of 2% per year.

12.3

The discount rate should be subjected to sensitivity analysis and be changed at the same rate of
0–4% per year for both cost and effectiveness.

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13 Modeling
13.1

To predict prognosis and future expenses, it is acceptable to conduct a model analysis using adecision analytic model, the Markov model, and/or other models in accordance with the principle
described in section “7.”13.2

The model analysis should present the validity of the model, including the following.

		(A) Internal validity: this addresses why a model with a given structure has been created,

whether the natural history of illness has been sufficiently evaluated, and whether the parameters
used are appropriate.
		(B) External validity: this addresses whether the estimation yielded from the model is appropri-

ate in comparison to other existing clinical data.

13.3

The assumption used to create the model should be clearly described.

13.4

All parameters and data sources used for model creation should be presented.

13.5

The model should be submitted in the form of electronic files. The model must be easily understood by third-party experts and all main parameters (transition probability, QOL score, andhealthcare costs) must be alterable.

13.5.1

It is ideal that not only total costs but also the breakdown (in the case of micro-costing, themedical resource consumption and unit costs of each item) can be changed. Especially,
the unit costs of the selected technology and comparator(s) must be adjusted for eachacademic analysis group.

13.6

Half-cycle correction should be used in the Markov model if the length of the Markov cycle is longand its influence on the results is not negligible.

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Uncertainty
14.1

If the analysis setting has multiple scenarios and this could affect the results, a scenario analysis
should be conducted.

14.2

For situations in which the uncertainty is high because of a long time horizon, a shorter-term analysis is necessary, such as an analysis of the period for which clinical study data are available.

14.3

If no available studies involve a comparison with the comparator according to section “5,” particularly when a comparison has been made concerning results between single-arm studies, a sensitivity analysis with a sufficiently wide range is required because of the large degree of uncertainty.

14.4

Sensitivity analyses are needed for parameters with large variances, those based on assumptions
rather than actual data, those with possible heterogeneity between domestic and other countries’
data, and others.

14.5

When the variance of the estimator should be considered (parametric uncertainty), the range
moving parameter in the sensitivity analysis can refer to the 95% confidence interval of theestimator.

14.6

A probabilistic sensitivity analysis (PSA) is also desirable. In such cases, the distribution used foranalysis, scatter plots of the cost-effectiveness plane, and cost-effectiveness acceptability curves(CEAC) must be presented.

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Terminology

Additional benefit
In a cost-effectiveness analysis, the additional benefit relative
to the comparator should be demonstrated before calculating the
ICER. The endpoint of effectiveness used to demonstrate the additional benefit does not always need to be equal to the outcome
used for the cost-effectiveness analysis but should be clinicallysignificant. If additional benefit is judged to be shown, cost-effectiveness analysis should be performed. On the other hand, if noadditional benefit is shown, cost of both treatment should be compared by so called “CMA”.

Cost-effectiveness analysis
Economic evaluations of healthcare technologies are often
divided into the following patterns: (a) cost-minimization analysis (CMA), in which the outcome is deemed equivalent and only
cost is analyzed; (b) cost-effectiveness analysis (CEA), which uses
outcome units other than QALY (LY, event avoidance, etc.); (c)cost-utility analysis (CUA), which uses QALY; and (d) cost-benefit
analysis (CBA), which involves an evaluation of outcomes afterconversion into monetary units.
However, CMA, CEA, and CUA can all be considered analogous in situations where the cost and outcome are estimated indifferent units. For this reason, these types of analysis are collectively termed “cost-effectiveness analyses” in this guideline.

Discounting
In a cost-effectiveness analysis, a discount at a constant rate isusually made to convert future costs and arising (or obtained) outcomes to current values. Costs converted to the current value after
applying yearly discounts (Cp) can be calculated from the costat i years later (Ci) and the discount rate (d) using the following
equation:CP=Ci(1+d) i-1

The same calculation can be used for effectiveness.

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Dominant/dominatedindividual who is unable to work. For this reason, one view suggests that productivity losses should include only friction costs (e.g.,

If a technology is lower in cost and equivalent or higher in ef-

based on the period needed to restore the initial production lev-

fectiveness than the comparator is, the technology is called “dom-el). Wages should be originally estimated through an investigation

inant.” If the technology is higher in cost but equivalent or lower inof the period for which an individual was actually unable to work

effectiveness relative to the comparator, the technology is called

because of illness. If this estimation is difficult due to lack of data

“dominated.”

including housework, it is acceptable to set the employment rate
at 100%. From the viewpoint of fairness, the mean wage across allindustries, all ages, and both genders should be used as the unit
wage, regardless of the actual unit wage for individuals.

Evidence level
Diverse classification methods for evidence levels are available.
MINDS (Medical Information Network Distribution Service) set

Incremental cost-effectiveness ratio

forth the following classification:
The incremental cost-effectiveness ratio (ICER) is the incremenI

Systematic review/meta-analysis of RCTsII

From one or more RCTs

III

From a non-randomized controlled study

IV aAnalytical epidemiological study (cohort study)

tal cost divided by the incremental effectiveness. ICER of treatment
A compared with B is calculated using the following equation:

ICER =IV b Analytical epidemiological study (case-control or

IC
IE

=

cross-sectional studies)

IC: incremental costV

Descriptive study (case reports or series)

IE: incremental effectivenessVI

Views of an expert committee or individual experts

CA: expected cost of treatment A

that are not based on patient data

CB: expected cost of treatment B

However, it has been often noted that the results of experimental studies such as randomized controlled trials (RCTs) can differ

CA - C B
EA - E B
EA: expected effectivenessof treatment A
EB: expected effectivenessof treatment B

ICER is an indicator of the cost to acquire one unit of effectiveness. A lower ICER indicates higher cost-effectiveness.

from real-world clinical data. Economic evaluations of healthcaretechnologies should primarily use data with a high level of evidence, although consideration should be given to appropriate
clinical data.

Indirect comparison
When clinical studies yield results for “A vs. B” and “A vs. C,” anestimation of the results for “B vs. C” in which no direct compari-

Human capital method

son is available from the head-to-head results is called an “indirectcomparison.” If no head-to-head study involving an appropriate

The “human capital method” is used to estimate productivity
loss based on the wages originally expected to be earned. How-

comparator is available, an indirect comparison may occasionallybe used.

ever, when viewed from a long-term standpoints, the inability of

The following conditions must be satisfied to enable indirect

an individual to work does not always lead to a productivity loss

comparison: the results for “A vs. B” must also be applicable to the

because in a situation with an employment rate less than 100%,

population “A vs. C” and the results for “A vs. C” must also be ap-

as other individuals are likely to work in place of the affectedplicable to the population “A vs. B.” This is called an “assumption

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of similarity.” When an indirect comparison is performed, it is nec-

curve (CEAC), defined as f(γ) = Pr(γ･IE − IC >0) (IC: incremental

essary to test this assumption and to use appropriate statisticalcost, IE: incremental effectiveness, γ: willingness to pay).

methods (for example, adjusted indirect comparison rather thannaïve indirect comparison). This approach also enables analyses
based on more advanced methods such as network meta-analyses (or multiple treatment comparisons; MTCs).

Productivity loss
Depending on the perspective, a loss resulting from the inability to perform work/housework because of illness (or benefit from

Mappingearly recovery) may be counted as a cost (i.e., productivity loss)
but is not included in the base-case analysis. It is acceptable toWhen preference-based measure-determined QOL scores are

consider not only the loss experienced directly by the patient but

unavailable, it is sometimes advantageous to use PRO data to cal-also losses experienced by family members or others arising fromculate the QOL score used for cost-effectiveness analysis. The con-the need to provide nursing or informal care. According to thisversion of scores between measures is called “mapping.” Mapping

guideline, however, an indirect productivity loss resulting from an

is acceptable as a second-best method when no other data are

improvement in the patient’s health states (e.g., survival period ex-available but should be performed only after sufficient assessment

tension) is not included in productivity loss to avoid double count-of the statistical validity.

ing (i.e., counting a factor as both effectiveness and costs). Only aproductivity loss directly attributable to the healthcare technology(e.g., shortened hospital stay) is permitted for inclusion.

Meta-analysis
Meta-analysis is a method by which the results from a systematic review are integrated statistically to yield integrated values or

Quality-adjusted life year

their confidence intervals. If the heterogeneity is small, a fixed-effect model is usually used. If the heterogeneity is large, random-ef-

A quality-adjusted life year (QALY) value is calculated by mul-fect or Bayesian models are usually employed. The results are

tiplying the life years (LYs) by the QOL score. A QOL score of 1

often depicted as forest plots. If a comparison is made amongindicates full health, whereas 0 indicates death. If an individual

multiple treatments rather than between two treatments (pairwise

has survived for two years under a health states with a QOL = 0.6,

comparison), a “network meta-analysis” is used, employing differ-

the LY is two years and the QALY is 0.6 × 2 = 1.2 (equivalent to 1.2

ent methods (ref. “Indirect comparison”).

years survival under full health). If the QOL score changes overtime, the QALY is represented by the area under the curve of the
QOL score over time, as illustrated in the figure below.

Probabilistic sensitivity analysis
Probabilistic sensitivity analysis (PSA) is a technique used todetermine the distributions of incremental cost, incremental effectiveness, and ICER by applying model parameters to the distribution. The results of a PSA are usually shown as a scatter plot on the
cost-effectiveness plane and as a cost-effectiveness acceptability27

Quality of life (QOL) score (utility)

Sensitivity analysis

The health states (i.e., value obtained from the health states) is

When uncertainty is present, its influence on the results canscored using a one-dimensional scale ranging from 0 (death) to

be evaluated by changing the parameter in a “sensitivity analysis.”

1 (full health). Negative scores, reflective of a health states “worse

Sensitivity analyses can be further classified as a one- (only one

than death,” are also possible.

parameter is changed) and two-dimensional (two parameters are

QOL scoring methods can be categorized as follows: (1) direct

simultaneously changed) sensitivity analyses, as well as PSA (si-methods that evaluate health states under a hypothetical situation

multaneous uncertainty in multiple parameters; see “Probabilistic(or about himself/herself), including the standard gamble (SG) andsensitivity analysis”).

time trade-off (TTO) methods, and (2) indirect methods that calculate QOL scores from patients’ responses to QOL questionnaires
using a scoring algorithm.
The QOL score used for cost-effectiveness analysis cannot al-

Systematic review

ways be calculated from any patient-reported outcome (PRO) or
QOL data. Cost-effectiveness analysis can utilize only QOL scores

A systematic review is a method by which the literature is com-

determined using a preference-based measure developed forprehensively searched regarding a specific topic and the results

QALY calculation, as described below.

are evaluated/reported without bias if at all possible. This meth-

The EQ-5D (EuroQol 5 dimension) is one currently available

od was defined by MINDS as follows: “When defined from themeasure for which a scoring algorithm has been developed in

aspects of practical actions, systematic review means ‘searching

Japan.

studies on a given clinical question comprehensively, groupingstudies of identical quality on each research design and analyzing/
integrating them being accompanied by evaluation of biases’.”A systematic review is often confused with meta-analysis. The
results of a systematic review do not always require statistical integration; this type of systematic review is also known as a “qualitative systematic review.” In cases where the integration of results
is deemed appropriate, a meta-analysis of the systematic review
results is needed.
Regarding the reporting style for a systematic review (meta-analysis), the style presented in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement
has been used as a standard and can be used as a reference.

Uncertainty
Various types of uncertainty accompany cost-effectivenessanalyses.
Broadly, heterogeneity is a type of uncertainty that indicates asituation lacking uniformity in terms of the comparator, healthcare

Center for Outcomes Research and Economic Evaluation for Health, National Institute of Public Health (C2H)

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patterns, targeted patients, and other factors. This differs fromthe uncertainty in the narrow sense, as explained below. This is
not a technical problem related to statistics or health economics
but rather arises from real-world variety. If such heterogeneity
is present, a sensitivity analysis based on multiple scenarios isrecommended.
Uncertainty in the narrow sense can be divided into (a) model and (b) parameter uncertainties. Model uncertainty can result
from (a)-1 methodological uncertainty and (a)-2 model structure/assumptions.
Methodological uncertainty, mentioned in (a)-1, arises fromthe theoretical impossibility of setting uniform methods for theestimation of the discount rate and productivity loss, measuring
the QOL score, and other parameters. To avoid this type of uncertainty, it is important to conduct an analysis in accordancewith common and standard procedures. If results such as thediscount rate are markedly affected, uncertainty should be evaluated through one-way sensitivity analysis.
Uncertainty arising from the model structure/assumption, as
mentioned in (a)-2, is caused by the method used to model thehealth states and treatment processes, selection of parameters
for incorporation into the model, assumptions regarding predictions of long-term prognosis beyond the observation period, andother factors. This uncertainty should be evaluated in a sensitivity analysis.
Parameter uncertainty, as mentioned in (b), arises from uncertainty inherent in the parameter estimation. For example, if 10
of 100 subjects develop events during a clinical study, the true
incidence rate might not be 10/100 = 0.1 in the whole population. To deal with this type of uncertainty, which is attributable tostatistical inference, it is useful to conduct a PSA in addition to a
deterministic sensitivity analysis.

Unrelated medical costs
Medical costs can be divided into related (i.e., those directly
affected by the selected technology) and unrelated (i.e., those affected indirectly through survival extension or those not related tothe illness). For example, a hypertension treatment that reduces
the incidence of cardiovascular disease and stroke will extend life
expectancy, possibly leading to an increase in unrelated medical
costs (e.g., costs related to dementia, diabetes, and hemodialysis).

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These unrelated costs are not included in the cost.

Abbreviations

CBA:

MTC:

Cost-benefit analysis

Multiple treatment comparison

CEA:

PBM:

Cost-effectiveness analysis

Preference-based measure

CEAC:

PRO:

Cost-effectiveness acceptability curve

Patient-reported outcome

CMA:

PRISMA:

Cost-minimization analysis

Preferred Reporting Items for
Systematic Reviews and Meta-Analyses

CSIMC:
Central Social Insurance Medical Council

PSA:(Chuikyo)

Probabilistic sensitivity analysis

CUA:

QALY:

Cost-utility analysis

Quality-adjusted life year

DCE:

RCT:

Discrete choice experiment

Randomized controlled trial

EQ-5D:

RQ:

EuroQol 5 dimension

Research question

HRQOL:

SG:

Health-related quality of life

Standard gamble

ICER:

SR:

Incremental cost-effectiveness ratio

Systematic review

MAIC:

TTO:

Matched adjusted indirect comparison

Time trade-off

Center for Outcomes Research and Economic Evaluation for Health, National Institute of Public Health (C2H)

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