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RESEARCH NOTE

Use of pharmacoeconomics in prescribing research.Part 3: cost-effectiveness analysis – a techniquefor decision-making at the margin

R. Lopert* BSc BMed MMedSc, D. L. Lang BMath BEc PostgradDipHlthEcEv

and S. R. Hill BMed PhD FAFPHM

*Pharmaceutical Benefits Branch, Department of Health and Ageing, Canberra, ACT, Australia and

Discipline of Clinical Pharmacology, Faculty of Health, University of Newcastle, NSW, Australia

SU M M A RY

This is the third Research Note addressing phar-

macoeconomics in prescribing research, reflecting

the increasing use of economic evaluation in drug

purchasing decisions in a variety of settings. Inthis segment we provide an overview of the the-

oretical basis, practical application and meth-

odological limitations of cost-effectiveness

analysis (CEA).

Keywords: allocative efficiency, cost-effective-

ness analysis, economic evaluation, opportunity

cost, pharmacoeconomics, technical efficiency

INT RODU CT ION

Economics is fundamentally about optimizing the

distribution of limited resources – making choices,

recognizing opportunity costs and maximizing

efficiency. Choosing to direct resources to (i.e.spend money on) a particular health care inter-

vention means that other interventions may need to

be modified, delayed or even abandoned. In max-

imizing efficiency the benefit to be derived from

the expenditure decision should exceed the benefit

to be gained by any alternative (1). Although eco-

nomic evaluation is an aid to making rational

decisions in health care, it cannot be regarded as a

panacea for making difficult choices. Other factors

apart from efficiency, such as access to drugs,

clinical need and who benefits will also influence

resource allocation decisions.

The term cost-effectiveness analysis (CEA) is often

used generically to refer to all forms of economic

evaluation, but is in fact only one of several tech-

niques that may be applied to the evaluation of the

benefits and costs of drugs and other health tech-

nologies. Other techniques of economic evaluation –

cost benefit analysis (CBA), cost minimization

analysis (CMA) and cost utility analysis (CUA) –

may also be applied in the context of drug selection

(hence pharmacoeconomic evaluation) to help make

explicit the costs and consequences of resourceallocation decisions. These are discussed in other

articles in this series.

The various techniques all share a common,

overarching objective – to relate the outcomes of

health care interventions to the costs associated

with their use. Where they differ is in the way in

which the outcomes are measured and valued, and

Box 1.

Key message box

Cost-effectiveness analysis is a technique to aid

decision-making at the margin.

Cost-effectiveness analysis is most readily applicable toquestions of technical efficiency – questions of

allocative efficiency can only be addressed where

there is a common outcome measure.

Difficulties of interpretation can occur when

cost-effectiveness ratios are presented in terms of

surrogate or intermediate endpoints.

Series Editor: Paramjit Gill, University of Birmingham

Received 27 November 2002, Accepted 3 January 2003

Correspondence: Dr Ruth Lopert, Pharmaceutical Benefits

Branch, Department of Health and Ageing, Canberra ACT,

Australia. Tel.: +612 6289 4111; fax: +612 8289 8633; e-mail:

[email protected]

Journal of Clinical Pharmacy and Therapeutics (2003) 28, 243–249

2003 Blackwell Publishing Ltd 243



whether the resource allocation issue in question is

one of allocative efficiency or technical efficiency (2).

Allocative efficiency is concerned with whether to

allocate scarce resources to a programme or whe-

ther to allocate more or less resources to it. Allocative

efficiency addresses the mix and type of services

that maximize the health gain of a society. Tech-

nical efficiency is concerned with how best to deliver

a programme or to achieve a given objective in

circumstances where a decision has already been

made to allocate resources for a new or existing

programme (Table 1).

Where the expected health benefits of two drugs

(or other health technologies) are similar, then

attention may be focused on the comparative costs

in order to identify the least cost option. This

technique – CMA – was described in a previous

article in this series [Newby D and Hill S (2003) Use

of pharmacoeconomics in prescribing research.Part 2: cost minimization analysis – when are two

therapies equal? Journal of Clinical Pharmacy and

Therapeutics 28, 145–150]. If, however, the outcomes

are not expected to be the same, then both costs and

consequences of alternative options need to be

considered. CEA, CBA and CUA are all techniques

that enable us to do this (Table 2).

W HA T IS CEA ?

Generally speaking CEA is a technique that is most

appropriately applied when a choice must be made

between two or more competing options for which

the expected health gains can be expressed in terms

of a common outcome measure. CEA has been

described as a technique for making decisions at the

margin, in situations where the question may be

framed as, ‘Is it worth spending an additional $x or

£y to achieve the additional benefits offered by the

new drug compared to existing therapy?’ It istherefore primarily a technique for addressing

Table 1. Questions of efficiency and economic evaluation technique

Efficiency Example Appropriate technique

Allocative efficiency Cox-2 inhibitors for arthritis vs. HmG-CoA reductase inhibitors

(‘statins’) for prevention of coronary heart disease

Cost benefit analysis

Cost utility analysis

Technical efficiency Cox-2 inhibitors vs. NSAIDs for the management

of rheumatoid arthritis

Cost minimization analysis

(if no difference in benefit)

Cost-effectiveness analysis

Cox-2, cyclooxygenase-2; NSAIDS, non-steroidal anti-inflammatory drugs; HmG-CoA, hydroxy-methyl-glutaryl coenzyme.

Table 2. Types of economic evaluation

Method Example of an appropriate question Outcomes Measure

Cost minimization

analysis

Of two Cox-2 inhibitor drugs with

equal effectiveness, which is the least expensive?

Equivalent None

Cost effectiveness

analysis

Streptokinase has different costs and effects

to tissue plasminogen activator in thrombolysis

for acute myocardial infarction

What is the incremental cost per life

year gained of SK compared with TPA?

Unidimensional Natural units

(life-years gained)

Cost utility

analysis

A low molecular weight heparin offers survival and

quality of life gains but at a higher cost than

unfractionated heparin in patients with

unstable coronary artery disease.

What is the cost per quality adjusted life year gained

of therapy with LMWH compared with UFH?

Multidimensional Health index

(quality adjusted

life years – QALYs)

Cost benefit

analysis

What is the ratio of cost to benefit for SK vs. LMWH? Multidimensional Commensurate

($, 2, ¥)

2003 Blackwell Publishing Ltd, Journal of Clinical Pharmacy and Therapeutics, 28 , 243–249

244 R. Lopert et al.



questions of technical efficiency (how best to deliver a

programme or to achieve a given objective).

SOM E DEF INIT IONS ( A ND F ORM U LA E)

To appreciate the role of CEA within economic

evaluation more generally, some definitions are

helpful. First, a cost-effectiveness ratio (CER) is a

method of calculating the cost per unit of benefit of

a drug or other therapeutic intervention. It is the

ratio of the resources used per unit of benefit of the

drug or intervention in question and implies that

the calculation has been made relative to ‘no

treatment’ – although no treatment usually has

costs and effects that should be taken into account

in the CEA (3). This ratio, when calculated relative

to no treatment, is sometimes referred to as an

‘average’ or ‘absolute’ CER:

Average CER ¼ CostA=EffectA

However, an average CER calculated in isolation

may be of limited usefulness. In most cases we are

interested in establishing the net cost-effectiveness of

an intervention – its costs and health outcomes,

compared with some alternative, such as the treat-

ment most likely to be replaced by the intervention.

A marginal CER refers to the change in costs and

health benefits from a one-unit expansion or con-

traction of service from a particular health care

intervention (2).

Marginal CER ¼ CostnA Costð

n

1Þ

AEffectnA Effectðn1ÞA

A well-known example of marginal CEA is Neu-

hauser and Lewicki’s analysis of the sixth stool

guaiac test for screening of colon cancer (4). The

analysis demonstrated (see Table 3) that the cost of

detecting cancer with each subsequent test rises

exponentially so that the marginal CER of the sixth

test compared with the fifth test ($47Æ1 million per

addition cancer detected) may be 20 000 times the

average CER ($2451 per cancer detected).

An incremental CER represents the change in costsand health benefits when one health care interven-

tion is compared with an alternative one (2) (Table 4).

Incremental CER ¼ CostA CostB

EffectA EffectB

Table 3. Average vs. marginal CERS – the sixth stool guaiac test*

Tests

Cancers detected Costs Cost-effectiveness ratios

Number of

cases (A)

Marginal

cases (B)

Total

costs (C)

Marginal

costs (D)

Average

CER (C ⁄

A)

Marginal

CER (D ⁄

B)

1 65Æ9469 64Æ9469 $77 511 $77 511 $1175 $1175

2 71Æ4424 5Æ4956 $107 690 $30 179 $1507 $5492

3 71Æ9004 0Æ4580 $130 199 $22 509 $1810 $49 140

4 71Æ9385 0Æ0382 $148 116 $17 917 $2059 $469 150

5 71Æ9417 0Æ0032 $163 141 $15 024 $2268 $4 724 695

6 71Æ9420 0Æ0003 $176 331 $13 190 $2451 $47 107 214

*Source: Neuhauser and Lewicki (4).

Table 4. Average, marginal and incremental cost-effective analyses

Question Intervention ComparatorCost-effectivenessratio

What is the cost per unit of benefit

from six stool guaiac tests?

Six stool guaiac tests No test Average CER

What is the additional cost per unit

of benefit of the sixth stool guaiac test?

Six stool guaiac tests Five stool guaiac tests Marginal CER

What is the additional cost per unit

of benefit of colonoscopy vs. stool guaiac testing?

Colonoscopy Stool guaiac test Incremental CER

2003 Blackwell Publishing Ltd, Journal of Clinical Pharmacy and Therapeutics, 28, 243–249

Cost-effectiveness analysis 245



If drug A is clearly superior to drug B and costs

less, then the decision is relatively straightforward.

In this case, drug A is said to be dominant over drug

B. If drug A offers less benefit at greater cost then the

choice is again straightforward – why pay more if

you expect to derive less benefit? If drug A offers

additional benefit at a higher cost then our calcu-

lation of an incremental cost-effectiveness ratio

(ICER) comes into play. The question is then: Are

the extra benefits to be gained from using this drug

worth the additional costs? This involves a difficult

value judgement; what is an acceptable CER for

one person, or in one setting or at one time, may be

unacceptable in another (3). In order to attempt to

answer this question we calculate the ICER – a

means of expressing the additional cost (or

expenditure required) to deliver each incremental

unit of benefit (Table 5).

COM PONENT S OF A CEA

Several guidelines exist for reviewing a published

or submitted CEA, or for preparing your own (2, 5,

6). The key components of a CEA are summarized

below.

Context

A description of the intervention, the population

and setting in which it is to be used, and the

appropriate comparator should be identified and

justified. The population in the CEA may be

identified by, for example, age, gender and ⁄ or

clinical history (6). The description of the setting

may include the location and type of institution

(hospital or primary care) (6). Appropriate com-

parators for the CEA may be the most cost-

effective alternative currently available, or the

therapy most likely to be replaced by the new

intervention (5). For a new drug, this may be a

drug in the same therapeutic class, a drug

belonging to a different therapeutic class or a non-

drug therapy where this represents standard

medical management of the condition in question

(7). The choice of an appropriate comparator is

critical in CEA (8). The nomination of an expen-

sive comparator may make the new intervention

appear more cost-effective than it should, leading

to an underestimate of the true opportunity costof its adoption.

Evidence of comparative efficacy

It is important to include the search strategies and

inclusion criteria used to identify studies that pro-

vide evidence of treatment efficacy for incorpor-

ation into the CEA, and to consider the design and

evidentiary quality that these studies represent (9).

The results (together with appropriate confidence

intervals for the estimates of effect) should be

Table 5. An incremental cost-effectiveness ratio using data from the GUSTO trial

t-PA Streptokinase Difference

One-year survival 91Æ0% 89Æ9% 1Æ1%

95% CI (0Æ46–1Æ74%)

P ¼ 0Æ006

Average costs of treatment over 1 year $27 740 $24 895 $2845

Incremental cost per patient surviving at 1 year $2845 ⁄ 0Æ011 ¼ $258 636

Projected survival 15Æ41 years 15Æ27 years 0Æ14 years

Incremental cost per year of life saved

(undiscounted)

$20 321

Incremental cost per year of life saved

(with costs and benefits discounted at 5%)

$32 678

In the Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries (GUSTO) trial patients with

acute myocardial infarction who were treated with accelerated tissue plasminogen activator (t-PA) had a 30-day mortality that was 15%

lower than that of patients treated with streptokinase. This was equivalent to an absolute decrease of 1% in 30-day mortality. One year after

enrolment, patients whoreceivedt-PA hadboth highercosts($2845) anda highersurvivalrate (anincreaseof 1Æ1%, or11 per 1000 patients

treated) than streptokinase-treated patients. On the basis of the projected life expectancy of each treatment group, the incremental cost-

effectiveness ratio – with both future costs and benefits discounted at 5% per year – was $32 678 per year of life saved (21).





presented clearly. In calculating any incremental

CER, we are interested in the absolute difference in

benefit (EffectA – EffectB) of the interventions being

compared. This will be given by the absolute risk

difference for binary outcomes and the difference in

mean values for continuous data (Table 6).

The cost-effectiveness of a treatment will vary

with the degree of benefit that treatment offers. Thegreater the degree of benefit for a given cost,

the more cost-effective an intervention will be. The

degree of benefit offered by an intervention will in

turn depend on the baseline risk; those at higher

risk of an event have greater capacity to benefit

from treatment. To illustrate this point consider the

cost-effectiveness of ‘statin’ treatment of hyper-

cholesterolaemia. Pharoah and Hollingworth (10)

estimated the average cost-effectiveness of statin

therapy ranged from £15 000 to £70 000 in men

with pre-existing coronary heart disease. For men

without coronary heart disease, average cost-

effectiveness ranged from £70 000 to £424 000 (10).

Costs

For the numerator of the CER, we are interested

in the relevant differences in costs between the

two treatments under consideration (CostA –

CostB). There are three steps in determining the

costs: identification, measurement and valuation.

The perspective of the analysis determines the

range of costs to be included (identification). Thesocietal perspective is the most comprehensive

perspective, but often more limited perspectives

(patient, health care programme, government, or

other third party payer) are adopted. The num-

ber of units consumed (measurement) of each

resource for the intervention and its comparator

should be totalled and the unit costs for each

resource (valuation) identified separately. The

currency and year of the unit cost data should

also be provided in the report of the CEA, and

the sources of data on resource used and unit

costs. A comprehensive review of identifying,

measuring and valuing costs in CEA, is the

subject of the first paper in the series (11).

Resource utilization data for incorporation into

CEAs are increasingly being collected prospec-tively in clinical trials. This raises the issues of trial

design, in particular whether sample size calcula-

tions should take into account the requirements of

the economic and the clinical evaluation (12). There

is as yet no consensus on the correct method for the

calculation of an appropriate sample size or

determining the power of a trial of a given size if

economic endpoints are to be taken into account

(13, 14).

Appropriate time horizon ⁄ discounting

The analysis should state clearly the timespan or

time horizon of the CEA, and this should cover the

time period over which the health benefits and

resource utilization will accrue. When costs and

benefits extend over a number of years, discount-

ing should be used to reflect the fact that values

from today’s perspective depend on when costs are

incurred and benefits accrue (11). Typical discount

rates range from 3 to 6%. The effect of using dis-

count rates may be explored in sensitivity analyses.

Incremental analysis

The results of the incremental or marginal CEA

should be provided in both disaggregated and

aggregated form. That is, the costs and benefits of

each alternative should be presented, along with

the incremental costs and incremental benefits, and

the incremental CER.

Table 6. Unfractionated heparin vs. low molecular weight heparin

Outcome

Low molecular

weight heparin

Unfractionated

heparin Relative risk

Absolute risk

difference

Combined risk of death,

AMI or unstable angina

318 ⁄ 1607 (19Æ8%) 364 ⁄ 1564 (23Æ3%) 19Æ8% ⁄ 23Æ3% ¼ 0Æ85 23Æ3 ) 19Æ8% ¼ 3Æ5%

Source: Cohen et al. (22).

AMI, acute myocardial infarction.





The interpretation of incremental CERs can be

difficult. Clinical trials often measure and report

surrogate or short-term endpoint data rather than

results measured against major clinical endpoints

such as death, survival, disability or cure. A sur-

rogate endpoint is a laboratory measure or disease

marker which is relatively easily measured and

which is thought to predict the clinically relevant

outcome(s) of a therapeutic intervention (15, 16).

The use of surrogate endpoints can considerably

reduce the sample size, duration, and cost of clin-

ical trials, and can allow treatments to be assessed

in situations where the use of clinical endpoints

might be considered excessively invasive or

unethical (15).

Where outcomes are presented in terms of

surrogate or intermediate endpoints, the results

of CEAs may be difficult to interpret. Even where

a surrogate endpoint is considered to be well-validated, the interpretation of its value may yet

be difficult. How, for example, should we inter-

pret an incremental cost per additional mm of Hg

of systolic blood pressure lowered, or per 1%

reduction in HbA1c? Furthermore a difference in

treatment effect demonstrated within a clinical

trial may be statistically significant, and may be

achievable at a modest increment in cost, but

may not necessarily be clinically meaningful or

worthwhile.

Even where a CEA presents results in terms of

clinically meaningful endpoints, the decision may

not be straightforward. CEA is highly context

dependent. An ICER of £10 000 per death averted,

for example, may seem reasonable in developed

country setting, but it nevertheless implies an

underlying judgement as to the value of avoiding a

death within the context in which the analysis takes

place.

Sensitivity analyses

It is important when considering estimates of cost-effectiveness to determine the sensitivity of the

estimates to variations in both costs and treatment

effects. At a minimum, CERs should be varied

around the confidence interval of the point esti-

mate of treatment effect. For each sensitivity ana-

lysis, the choice of variables to be varied, and the

range over which each variable is varied, should be

provided, with justification.

In recent years, considerable effort has been

expended in developing methods for addressing

uncertainty in CEA. This has encompassed meth-

ods for estimating confidence intervals for CERs,

and other, broader approaches to analysing

uncertainty in CEA (17, 18).

Financial implications

The adoption of a therapy with even a modest

ICER may involve expenditure which exceeds a

given budget (and is therefore unaffordable) or

which precludes expenditure within other pro-

grammes. It is therefore important, when con-

ducting CEAs, to also include the financial

implications of the introduction of the new drug or

other health care intervention.

LIM IT A T IONS OF CEA

Cost-effectiveness analysis is the most frequently

used economic evaluation technique; it is concep-

tually straightforward, perhaps deceptively so.

CERs are generally simple to calculate and are

often expressed in terms of outcomes routinely

collected in clinical trials (19).

Cost-effectiveness analysis nevertheless has a

number of limitations. As has already been noted,

it may useful in determining expenditure priorities

for different treatments for the same condition

(technical efficiency) but it is much less easily

applied to decisions involving treatments for dif-

ferent diseases. Comparisons are not possible be-

tween programmes (or even within the same

programme) where there is no common metric. We

cannot compute comparative cost-effectiveness for

an antihypertensive therapy and an asthma medi-

cation where the treatment effects are reported in

terms of reduction in blood pressure in the first

instance and percentage increase in forced expira-

tory volume (FEV1) for the second.

Even where it is possible to measure and incor-porate final clinical outcomes such as life years

gained, or deaths averted, into the assessment of

cost-effectiveness we are still left with a unidi-

mensional measure that cannot combine reductions

in morbidity or improvement in quality of life with

survival gains into a single index, which is neces-

sary if we are to be able to compare treatments that

vary on both dimensions. In order to incorporate





this multidimensionality the more appropriate

technique is CUA (20). CUA takes into account not

just the number of years (survival) gained but the

quality of life of those years. CUA can be used to

assess technical efficiency but also allocative effi-

ciency within the health care sector. The technique

is discussed in more detail in Part 4 of this series.

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