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Journal of Clinical Pharmacy and Therapeutics (2003) 28, 243–249
RESEARCH NOTE
Use of pharmacoeconomics in prescribing research.
Part 3: cost-effectiveness analysis – a technique
for 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
SUMMARY
INTRODUCTION
This is the third Research Note addressing pharmacoeconomics in prescribing research, reflecting
the increasing use of economic evaluation in drug
purchasing decisions in a variety of settings. In
this segment we provide an overview of the theoretical basis, practical application and methodological limitations of cost-effectiveness
analysis (CEA).
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 intervention means that other interventions may need to
be modified, delayed or even abandoned. In maximizing efficiency the benefit to be derived from
the expenditure decision should exceed the benefit
to be gained by any alternative (1). Although economic 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 techniques that may be applied to the evaluation of the
benefits and costs of drugs and other health technologies. 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 resource
allocation 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 to
questions 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.
Keywords: allocative efficiency, cost-effectiveness analysis, economic evaluation, opportunity
cost, pharmacoeconomics, technical efficiency
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]
2003 Blackwell Publishing Ltd
243
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R. Lopert et al.
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 whether 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. Technical 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).
WHAT 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 $x or
£y to achieve the additional benefits offered by the
new drug compared to existing therapy?’ It is
therefore primarily a technique for addressing
Table 1. Questions of efficiency and economic evaluation technique
Efficiency
Example
Appropriate technique
Allocative efficiency
Cox-2 inhibitors for arthritis vs. HmG-CoA reductase inhibitors
(‘statins’) for prevention of coronary heart disease
Cox-2 inhibitors vs. NSAIDs for the management
of rheumatoid arthritis
Cost benefit analysis
Cost utility analysis
Cost minimization analysis
(if no difference in benefit)
Cost-effectiveness analysis
Technical efficiency
Cox-2, cyclooxygenase-2; NSAIDS, non-steroidal anti-inflammatory drugs; HmG-CoA, hydroxy-methyl-glutaryl coenzyme.
Table 2. Types of economic evaluation
Method
Example of an appropriate question
Outcomes
Measure
Cost minimization
analysis
Cost effectiveness
analysis
Of two Cox-2 inhibitor drugs with
equal effectiveness, which is the least expensive?
Streptokinase has different costs and effects
to tissue plasminogen activator in thrombolysis
for acute myocardial infarction
What is the incremental cost per life
year gained of SK compared with TPA?
A low molecular weight heparin offers survival and
quality of life gains but at a higher cost than
unfractionated heparin in patients with
unstable coronary artery disease.
What is the cost per quality adjusted life year gained
of therapy with LMWH compared with UFH?
What is the ratio of cost to benefit for SK vs. LMWH?
Equivalent
None
Unidimensional
Natural units
(life-years gained)
Multidimensional
Health index
(quality adjusted
life years – QALYs)
Multidimensional
Commensurate
($, 2, ¥)
Cost utility
analysis
Cost benefit
analysis
2003 Blackwell Publishing Ltd, Journal of Clinical Pharmacy and Therapeutics, 28, 243–249
Cost-effectiveness analysis
questions of technical efficiency (how best to deliver a
programme or to achieve a given objective).
SOME DEFINITIONS (AND FORMULAE)
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,
245
compared with some alternative, such as the treatment 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 contraction of service from a particular health care
intervention (2).
CostnA Costðn1ÞA
Marginal CER ¼
EffectnA Effectðn1ÞA
A well-known example of marginal CEA is Neuhauser 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 costs
and health benefits when one health care intervention 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*
Cancers detected
Costs
Cost-effectiveness ratios
Tests
Number of
cases (A)
Marginal
cases (B)
Total
costs (C)
Marginal
costs (D)
Average
CER (C ⁄ A)
Marginal
CER (D ⁄ B)
1
2
3
4
5
6
65Æ9469
71Æ4424
71Æ9004
71Æ9385
71Æ9417
71Æ9420
64Æ9469
5Æ4956
0Æ4580
0Æ0382
0Æ0032
0Æ0003
$77 511
$107 690
$130 199
$148 116
$163 141
$176 331
$77
$30
$22
$17
$15
$13
$1175
$1507
$1810
$2059
$2268
$2451
$1175
$5492
$49 140
$469 150
$4 724 695
$47 107 214
511
179
509
917
024
190
*Source: Neuhauser and Lewicki (4).
Table 4. Average, marginal and incremental cost-effective analyses
Question
Intervention
Comparator
Cost-effectiveness
ratio
What is the cost per unit of benefit
Six stool guaiac tests No test
Average CER
from six stool guaiac tests?
What is the additional cost per unit
Six stool guaiac tests Five stool guaiac tests Marginal CER
of benefit of the sixth stool guaiac test?
What is the additional cost per unit
Colonoscopy
Stool guaiac test
Incremental CER
of benefit of colonoscopy vs. stool guaiac testing?
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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 calculation 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).
COMPONENTS 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 comparators for the CEA may be the most costeffective 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 nondrug 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 expensive comparator may make the new intervention
appear more cost-effective than it should, leading
to an underestimate of the true opportunity cost
of its adoption.
Evidence of comparative efficacy
It is important to include the search strategies and
inclusion criteria used to identify studies that provide evidence of treatment efficacy for incorporation 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%
Average costs of treatment over 1 year
Incremental cost per patient surviving at 1 year
Projected survival
Incremental cost per year of life saved
(undiscounted)
Incremental cost per year of life saved
(with costs and benefits discounted at 5%)
$27 740
$24 895
15Æ41 years
15Æ27 years
1Æ1%
95% CI (0Æ46–1Æ74%)
P ¼ 0Æ006
$2845
$2845 ⁄ 0Æ011 ¼ $258 636
0Æ14 years
$20 321
$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 who received t-PA had both higher costs ($2845) and a higher survival rate (an increase of 1Æ1%, or 11 per 1000 patients
treated) than streptokinase-treated patients. On the basis of the projected life expectancy of each treatment group, the incremental costeffectiveness ratio – with both future costs and benefits discounted at 5% per year – was $32 678 per year of life saved (21).
2003 Blackwell Publishing Ltd, Journal of Clinical Pharmacy and Therapeutics, 28, 243–249
Cost-effectiveness analysis
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Table 6. Unfractionated heparin vs. low molecular weight heparin
Outcome
Combined risk of death,
AMI or unstable angina
Low molecular
weight heparin
Unfractionated
heparin
Relative risk
Absolute risk
difference
318 ⁄ 1607 (19Æ8%)
364 ⁄ 1564 (23Æ3%)
19Æ8% ⁄ 23Æ3% ¼ 0Æ85
23Æ3 ) 19Æ8% ¼ 3Æ5%
Source: Cohen et al. (22).
AMI, acute myocardial infarction.
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. The
greater 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 hypercholesterolaemia. 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 costeffectiveness 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). The
societal perspective is the most comprehensive
perspective, but often more limited perspectives
(patient, health care programme, government, or
other third party payer) are adopted. The number 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 prospectively in clinical trials. This raises the issues of trial
design, in particular whether sample size calculations 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, discounting 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 discount 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.
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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 surrogate 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 clinical 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 wellvalidated, the interpretation of its value may yet
be difficult. How, for example, should we interpret 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 costeffectiveness 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 estimate of treatment effect. For each sensitivity analysis, 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 methods 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 programmes. It is therefore important, when conducting CEAs, to also include the financial
implications of the introduction of the new drug or
other health care intervention.
LIMITATIONS OF CEA
Cost-effectiveness analysis is the most frequently
used economic evaluation technique; it is conceptually 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 different diseases. Comparisons are not possible between 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 medication where the treatment effects are reported in
terms of reduction in blood pressure in the first
instance and percentage increase in forced expiratory volume (FEV1) for the second.
Even where it is possible to measure and incorporate final clinical outcomes such as life years
gained, or deaths averted, into the assessment of
cost-effectiveness we are still left with a unidimensional measure that cannot combine reductions
in morbidity or improvement in quality of life with
survival gains into a single index, which is necessary if we are to be able to compare treatments that
vary on both dimensions. In order to incorporate
2003 Blackwell Publishing Ltd, Journal of Clinical Pharmacy and Therapeutics, 28, 243–249
Cost-effectiveness analysis
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 efficiency within the health care sector. The technique
is discussed in more detail in Part 4 of this series.
11.
12.
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