Use of cost-effectiveness acceptability curves, as a method for summarising information on uncertainty in cost-effectiveness, has become widespread within applied studies. This includes several studies in the mental health field. This editorial uses examples from recent papers to illustrate how cost-effectiveness acceptability curves are constructed, what they represent and how they should be interpreted

Briggs

Elisabeth Fenwick

Sarah Byford

English

Fenwick, E.

Byford, S.

Enlighten: Publications

 
 
 
 
 
 
Fenwick, E. and Byford, S. (2005) A guide to cost-effectiveness 
acceptability curves. British Journal of Psychiatry 187:pp. 106-108.
 
 
 
 
 
 
 
http://eprints.gla.ac.uk/4162/ 
 
12th May 2008 
 
 
Glasgow ePrints Service 
https://eprints.gla.ac.uk 
Summary Use of cost-effectiveness
acceptabilitycurves, as amethod for
summarisinginformation onuncertaintyin
cost-effectiveness, hasbecome
widespreadwithin applied studies.This
includes several studies inthemental
health field.This editorialuses examples
fromrecentpapers to illustrate howcost-
effectiveness acceptabilitycurves are
constructed, whattheyrepresent and
how they should be interpreted.
Declaration of interest None.
Mounting pressure on healthcare budgets
has led to an increased emphasis on eco-
nomic evidence to guide healthcare policy
and practice decisions. This has meant an
increase in demand for information
concerning the costs and effectiveness of
health technologies in order to determine
their cost-effectiveness. The aim of cost-
effectiveness analysis is to identify efficient
use of scarce healthcare resources, through
identifying the treatments and technologies
that provide the maximum additional
effects per additional unit of resource con-
sumed. The same principles apply to the
treatments, programmes and technologies
that comprise mental health services, and
there is a growing literature concerning
the cost-effectiveness of these various
services.
The cost-effectiveness acceptability
curve (CEAC) is a relatively new concept
that is featuring more frequently in cost-
effectiveness papers within the medical
literature. These curves illustrate the
uncertainty surrounding the estimate
of cost- effectiveness and were developed
as a result of considerable debate regarding
the best way to deal with such uncertainty
(Van Hout et al, 1994; Briggs & Fenn,
1998; Briggs & Gray, 1999; O’Brien &
Briggs, 2002). Since their conception, use
of CEACs has become widespread within
applied studies, including a number in the
mental health field (Bower et al, 2003;
Byford et al, 2003; Haddock et al, 2003;
Miller et al, 2003; Scott et al, 2003;
McCrone et al, 2004). It has thus become
important to understand what CEACs look
like, how they are constructed, what they
represent and how they should be
interpreted. We discuss these issues with
reference to recent papers (from this jour-
nal) that have included CEACs (Haddock
et al, 2003; Scott et al, 2003; McCrone et
al, 2004). We focus on CEACs derived
from comparisons of two interventions.
For evaluations comparing more than two
interventions, see Fenwick et al (2001).
WHATDOES ACEAC LOOK
LIKE?
A CEAC shows the probability that an
intervention is cost-effective compared with
the alternative, given the observed data, for
a range of maximum monetary values (l)
that a decision-maker might be willing to
pay for a particular unit change in out-
come. A typical example of a CEAC is
illustrated in Scott et al (2003: Fig. 1). Their
figure shows the probability that cognitive
therapy is cost-effective compared with
standard clinical management with
antidepressants, for a range of monetary
values that a decision-maker might consider
the maximum acceptable to avoid a
depressive relapse. This range of maxi-
mum monetary values, expressed as £
per depressive relapse avoided, is given
on the x-axis. Given a specified value of
this ‘acceptable’ cost-effectiveness ratio (a
point on the x-axis), the CEAC shows
the probability that the data are
consistent with a true cost-effectiveness
ratio falling below that value (read off the
y-axis).
HOW IS ACEAC
CONSTRUCTED?
Cost-effectiveness acceptability curves were
introduced as an alternative to producing
confidence intervals around incremental
cost-effectiveness ratios (ICERs), which
can be statistically challenging (Van Hout
et al, 1994; Briggs & Fenn, 1998). The
CEAC is derived from the joint distribution
of incremental costs and incremental
effects. The most common technique for
estimating these joint distributions is non-
parametric bootstrapping of the observed
data, although other methods are available
(Van Hout et al, 1994; Lothgren &
Zethraeus, 2000; O’Brien & Briggs,
A guide to cost-effectiveness acceptability curves
ELISABETH FENWICK and SARAH BYFORD
AUTHOR’S PROOF
Fig.1 Scatter plot showing themean differences in costs and in the primary outcomemeasure (Global
Assessmentof Functioning) from the trial data using1000 bootstrap replicates (differencesbased on cognitive^
behavioural therapyminus control). (Taken from Haddock et al, 2003.)
For Evaluation Only.
Copyright (c) by Foxit Software Company, 2004 - 2007
Edited by Foxit PDF Editor
2002). A scatter plot of the bootstrapped
incremental costs and effect pairs can be
presented on the incremental cost-
effectiveness plane, as shown in Fig. 1
(taken from Haddock et al, 2003). This
illustrates the uncertainty surrounding the
estimates of expected costs (here in £) and
expected effects (Global Assessment of
Functioning (GAF) scores) associated with
the intervention (cognitive–behavioural
therapy and motivational intervention)
compared with the alternative (routine
treatment).
The incremental cost-effectiveness
plane is divided into four quadrants by
the origin, with each quadrant having a dif-
ferent implication for economic evaluation.
The SE quadrant, with negative costs and
positive effects, represents the position
where the intervention is more effective
and less costly than the alternative
(‘dominates’). Interventions falling in this
quadrant are always considered cost-
effective regardless of the maximum accep-
table ratio (l). The NW quadrant, with
positive costs and negative effects, repre-
sents the position where the intervention
is both more costly and less effective than
the alternative (‘dominated’). Interventions
falling in this quadrant are never considered
cost-effective regardless of l. The NE quad-
rant, with positive costs and positive
effects, and the SW quadrant, with negative
costs and negative effects, involve trade-
offs. These two quadrants represent the
situation where the intervention may be
cost-effective compared with the alterna-
tive, depending upon whether the ICER is
above or below the given value of l.
As illustrated in Fig. 1, the scatter plot
commonly covers all four quadrants,
indicating uncertainty about whether or
not the intervention is cost-effective, and
at what value it is cost-effective. The
purpose of the CEAC is to summarise this
uncertainty.
The CEAC is constructed by plotting
the proportion of the costs and effects pairs
that are cost-effective for a range of values
of l. This proportion is easily identifiable
from the incremental cost-effectiveness
plane as the proportion of points falling
to the south and east of a ray through the
origin with slope equal to l. The process
of constructing a CEAC begins by calculat-
ing this proportion with a ray of slope zero
(equivalent to the x-axis). The process is
repeated numerous times for rays of larger
and larger slopes, up to a maximum value
for l of infinity (equivalent to the y-axis).
Points in the NW quadrant are never
considered cost-effective and therefore
never counted. Points in the SE are always
considered cost-effective and therefore
always counted. As the slope of the ray is
increased from zero to infinity, points in
the NE and SW quadrants may or may
not be considered cost-effective depending
upon the value of l. For more details con-
cerning the shape of the CEAC see Fenwick
et al (2004).
To illustrate this using the example
from Haddock et al (2003), 693 out of
1000 bootstrap re-samples involved cost
savings (fell below the x-axis), hence
69.3% of the costs and effects pairs fell to
the south and east of a ray with slope zero,
and as a result the CEAC crosses the y-axis
at 69.3%. For a ray with a slope of £20 per
point increase in GAF score, the proportion
of the re-samples that were cost-effective
was 70%, and for a ray with a slope of
£655 per point increase in GAF score the
proportion was 90%. In this way, a CEAC
is generated.
HOW IS ACEAC
INTERPRETED?
The CEAC indicates the probability that
the intervention is cost-effective compared
with the alternative, given the data and
for a given value of the maximum accep-
table ratio (l). In the example of Haddock
et al (2003), given a maximum acceptable
ratio of £20 per point increase in GAF
score, the probability that cognitive–
behavioural therapy is cost-effective com-
pared with routine treatment is 0.7. This
is equivalent to stating that, given the data,
there is a 70% chance that the additional
cost of cognitive–behavioural therapy,
compared with routine treatment, is less
than £20 per point increase in GAF score.
Note the comparative nature of both state-
ments. It is not equivalent to stating that
cognitive–behavioural therapy has a 70%
chance of costing less than £20 per point
increase in GAF score.
Care must be taken when interpreting
the information provided by a CEAC. A
CEAC simply presents the probability that
an intervention is cost-effective compared
with the alternative for a range of values
of l. That is, the probability that the ICER
falls below the maximum acceptable ratio.
Statements concerning CEACs should be
restricted to the uncertainty of the estimate
of cost-effectiveness. The information from
a CEAC should not, in general, be used to
make statements about the implementation
of the intervention.
REFERENCES
Bower, P., Byford, S., Barber, J., et al (2003) Meta-
analysis of data on costs from trials of counselling in
primary care: using individual patient data to overcome
sample size limitations in economic analyses. British
Medical Journal, 326, 1247^1250.
Briggs, A. & Fenn, P. (1998) Confidence intervals or
surfaces? Uncertainty on the cost-effectiveness plane.
Health Economics, 7, 723^740.
Briggs, A. & Gray, A. (1999) Handling uncertainty
when performing economic evaluation of healthcare
interventions. HealthTechnology Assessment, 3, 2.
Byford, S., Knapp, M.,Greenshields, J., et al (2003)
Cost-effectiveness of brief cognitive behaviour therapy
versus treatment as usual in recurrent deliberate self-
harm: a rational decision making approach. Psychological
Medicine, 33, 977^986.
Fenwick, E.,Claxton, K. & Sculpher, M. (2001)
Representing uncertainty: the role of cost-effectiveness
acceptability curves. Health Economics, 10, 779^787.
Fenwick, E.,O’Brien, B. & Briggs, A. (2004) Cost-
effectiveness acceptability curves ^ facts, fallacies and
frequently asked questions. Health Economics, 13,
405^415.
Haddock,G., Barrowclough,C.,Tarrier, N., et al
(2003) Cognitive^behavioural therapy andmotivational
intervention for schizophrenia and substance misuse: 18-
month outcomes of a randomised controlled trial. British
Journal of Psychiatry, 183, 418^426.
Lothgren, M. & Zethraeus, N. (2000) Definition,
interpretation and calculation of cost-effectiveness
acceptability curves. Health Economics, 9, 623^630.
AUTHOR’S PROOF
ELISABETH FENWICK, PhD,Department of Economics and Related Studies,University of York;
SARAHBYFORD,MSc,Centre for the Economics of Mental Health, Institute of Psychiatry,De Crespigny Park,
London,UK
Correspondence:Dr Elisabeth Fenwick,Department of Economics and Related Studies,University of
York,Heslington,YorkYO10 5DD,UK.Tel: +44(0) 1904 321451; fax: +44(0) 1904 321402; e-mail:
ealf100@york.ac.uk
(First received 28 April 2004, final revision 14 September 2004, accepted 4 November 2004)
For Evaluation Only.
Copyright (c) by Foxit Software Company, 2004 - 2007
Edited by Foxit PDF Editor
McCrone, P., Knapp, M., Proudfoot, J., et al (2004)
Cost-effectiveness of computerised cognitive^
behavioural therapy for anxiety and depression in
primary care: randomised controlled trial. British Journal
of Psychiatry, 185, 55^62.
Miller, P., Chilvers, C., Dewey, M., et al (2003)
Counselling versus antidepressant therapy for the
treatment of mild to moderate depression in primary
care: economic analysis. International Journal of
Technology Assessment in Health Care, 19, 80^90.
O’Brien, B. & Briggs, A. (2002) Analysis of
uncertainty in health care cost-effectiveness studies: an
introduction to statistical issues and methods. Statistical
Methods in Medical Research, 11, 455^468.
Scott, J., Palmer, S., Paykel, E., et al (2003) Use of
cognitive therapy for relapse prevention in chronic
depression: cost-effectiveness study. British Journal of
Psychiatry, 182, 221^227.
Van Hout, B. A., Al, M. J.,Gordon,G. S., et al (1994)
Costs, effects and C/E-ratios alongside a clinical trial.
Health Economics, 3, 309^319.
AUTHOR’S PROOF
For Evaluation Only.
Copyright (c) by Foxit Software Company, 2004 - 2007
Edited by Foxit PDF Editor


A guide to cost-effectiveness acceptability curves

Use of cost-effectiveness acceptability curves, as a method for summarising information on uncertainty in cost-effectiveness, has become widespread within applied studies. This includes several studies in the mental health field. This editorial uses examples from recent papers to illustrate how cost-effectiveness acceptability curves are constructed, what they represent and how they should be interprete

Fenwick, E

Byford, S

King's Research Portal

Enlighten

      Fenwick, E. and Byford, S. (2005) A guide to cost-effectiveness acceptability curves. British Journal of Psychiatry 187:pp. 106-108.       http://eprints.gla.ac.uk/4162/  12th May 2008   Glasgow ePrints Service https://eprints.gla.ac.uk Summary Use of cost-effectivenessacceptabilitycurves, as amethod forsummarisinginformation onuncertaintyincost-effectiveness, hasbecomewidespreadwithin applied studies.Thisincludes several studies inthementalhealth field.This editorialuses examplesfromrecentpapers to illustrate howcost-effectiveness acceptabilitycurves areconstructed, whattheyrepresent andhow they should be interpreted.Declaration of interest None.Mounting pressure on healthcare budgetshas led to an increased emphasis on eco-nomic evidence to guide healthcare policyand practice decisions. This has meant anincrease in demand for informationconcerning the costs and effectiveness ofhealth technologies in order to determinetheir cost-effectiveness. The aim of cost-effectiveness analysis is to identify efficientuse of scarce healthcare resources, throughidentifying the treatments and technologiesthat provide the maximum additionaleffects per additional unit of resource con-sumed. The same principles apply to thetreatments, programmes and technologiesthat comprise mental health services, andthere is a growing literature concerningthe cost-effectiveness of these variousservices.The cost-effectiveness acceptabilitycurve (CEAC) is a relatively new conceptthat is featuring more frequently in cost-effectiveness papers within the medicalliterature. These curves illustrate theuncertainty surrounding the estimateof cost- effectiveness and were developedas a result of considerable debate regardingthe best way to deal with such uncertainty(Van Hout et al, 1994; Briggs & Fenn,1998; Briggs & Gray, 1999; O’Brien &Briggs, 2002). Since their conception, useof CEACs has become widespread withinapplied studies, including a number in themental health field (Bower et al, 2003;Byford et al, 2003; Haddock et al, 2003;Miller et al, 2003; Scott et al, 2003;McCrone et al, 2004). It has thus becomeimportant to understand what CEACs looklike, how they are constructed, what theyrepresent and how they should beinterpreted. We discuss these issues withreference to recent papers (from this jour-nal) that have included CEACs (Haddocket al, 2003; Scott et al, 2003; McCrone etal, 2004). We focus on CEACs derivedfrom comparisons of two interventions.For evaluations comparing more than twointerventions, see Fenwick et al (2001).WHATDOES ACEAC LOOKLIKE?A CEAC shows the probability that anintervention is cost-effective compared withthe alternative, given the observed data, fora range of maximum monetary values (l)that a decision-maker might be willing topay for a particular unit change in out-come. A typical example of a CEAC isillustrated in Scott et al (2003: Fig. 1). Theirfigure shows the probability that cognitivetherapy is cost-effective compared withstandard clinical management withantidepressants, for a range of monetaryvalues that a decision-maker might considerthe maximum acceptable to avoid adepressive relapse. This range of maxi-mum monetary values, expressed as £per depressive relapse avoided, is givenon the x-axis. Given a specified value ofthis ‘acceptable’ cost-effectiveness ratio (apoint on the x-axis), the CEAC showsthe probability that the data areconsistent with a true cost-effectivenessratio falling below that value (read off they-axis).HOW IS ACEACCONSTRUCTED?Cost-effectiveness acceptability curves wereintroduced as an alternative to producingconfidence intervals around incrementalcost-effectiveness ratios (ICERs), whichcan be statistically challenging (Van Houtet al, 1994; Briggs & Fenn, 1998). TheCEAC is derived from the joint distributionof incremental costs and incrementaleffects. The most common technique forestimating these joint distributions is non-parametric bootstrapping of the observeddata, although other methods are available(Van Hout et al, 1994; Lothgren &Zethraeus, 2000; O’Brien & Briggs,A guide to cost-effectiveness acceptability curvesELISABETH FENWICK and SARAH BYFORDAUTHOR’S PROOFFig.1 Scatter plot showing themean differences in costs and in the primary outcomemeasure (GlobalAssessmentof Functioning) from the trial data using1000 bootstrap replicates (differencesbased on cognitive^behavioural therapyminus control). (Taken from Haddock et al, 2003.)For Evaluation Only.Copyright (c) by Foxit Software Company, 2004 - 2007Edited by Foxit PDF Editor2002). A scatter plot of the bootstrappedincremental costs and effect pairs can bepresented on the incremental cost-effectiveness plane, as shown in Fig. 1(taken from Haddock et al, 2003). Thisillustrates the uncertainty surrounding theestimates of expected costs (here in £) andexpected effects (Global Assessment ofFunctioning (GAF) scores) associated withthe intervention (cognitive–behaviouraltherapy and motivational intervention)compared with the alternative (routinetreatment).The incremental cost-effectivenessplane is divided into four quadrants bythe origin, with each quadrant having a dif-ferent implication for economic evaluation.The SE quadrant, with negative costs andpositive effects, represents the positionwhere the intervention is more effectiveand less costly than the alternative(‘dominates’). Interventions falling in thisquadrant are always considered cost-effective regardless of the maximum accep-table ratio (l). The NW quadrant, withpositive costs and negative effects, repre-sents the position where the interventionis both more costly and less effective thanthe alternative (‘dominated’). Interventionsfalling in this quadrant are never consideredcost-effective regardless of l. The NE quad-rant, with positive costs and positiveeffects, and the SW quadrant, with negativecosts and negative effects, involve trade-offs. These two quadrants represent thesituation where the intervention may becost-effective compared with the alterna-tive, depending upon whether the ICER isabove or below the given value of l.As illustrated in Fig. 1, the scatter plotcommonly covers all four quadrants,indicating uncertainty about whether ornot the intervention is cost-effective, andat what value it is cost-effective. Thepurpose of the CEAC is to summarise thisuncertainty.The CEAC is constructed by plottingthe proportion of the costs and effects pairsthat are cost-effective for a range of valuesof l. This proportion is easily identifiablefrom the incremental cost-effectivenessplane as the proportion of points fallingto the south and east of a ray through theorigin with slope equal to l. The processof constructing a CEAC begins by calculat-ing this proportion with a ray of slope zero(equivalent to the x-axis). The process isrepeated numerous times for rays of largerand larger slopes, up to a maximum valuefor l of infinity (equivalent to the y-axis).Points in the NW quadrant are neverconsidered cost-effective and thereforenever counted. Points in the SE are alwaysconsidered cost-effective and thereforealways counted. As the slope of the ray isincreased from zero to infinity, points inthe NE and SW quadrants may or maynot be considered cost-effective dependingupon the value of l. For more details con-cerning the shape of the CEAC see Fenwicket al (2004).To illustrate this using the examplefrom Haddock et al (2003), 693 out of1000 bootstrap re-samples involved costsavings (fell below the x-axis), hence69.3% of the costs and effects pairs fell tothe south and east of a ray with slope zero,and as a result the CEAC crosses the y-axisat 69.3%. For a ray with a slope of £20 perpoint increase in GAF score, the proportionof the re-samples that were cost-effectivewas 70%, and for a ray with a slope of£655 per point increase in GAF score theproportion was 90%. In this way, a CEACis generated.HOW IS ACEACINTERPRETED?The CEAC indicates the probability thatthe intervention is cost-effective comparedwith the alternative, given the data andfor a given value of the maximum accep-table ratio (l). In the example of Haddocket al (2003), given a maximum acceptableratio of £20 per point increase in GAFscore, the probability that cognitive–behavioural therapy is cost-effective com-pared with routine treatment is 0.7. Thisis equivalent to stating that, given the data,there is a 70% chance that the additionalcost of cognitive–behavioural therapy,compared with routine treatment, is lessthan £20 per point increase in GAF score.Note the comparative nature of both state-ments. It is not equivalent to stating thatcognitive–behavioural therapy has a 70%chance of costing less than £20 per pointincrease in GAF score.Care must be taken when interpretingthe information provided by a CEAC. ACEAC simply presents the probability thatan intervention is cost-effective comparedwith the alternative for a range of valuesof l. That is, the probability that the ICERfalls below the maximum acceptable ratio.Statements concerning CEACs should berestricted to the uncertainty of the estimateof cost-effectiveness. The information froma CEAC should not, in general, be used tomake statements about the implementationof the intervention.REFERENCESBower, P., Byford, S., Barber, J., et al (2003) Meta-analysis of data on costs from trials of counselling inprimary care: using individual patient data to overcomesample size limitations in economic analyses. BritishMedical Journal, 326, 1247^1250.Briggs, A. & Fenn, P. (1998) Confidence intervals orsurfaces? Uncertainty on the cost-effectiveness plane.Health Economics, 7, 723^740.Briggs, A. & Gray, A. (1999) Handling uncertaintywhen performing economic evaluation of healthcareinterventions. HealthTechnology Assessment, 3, 2.Byford, S., Knapp, M.,Greenshields, J., et al (2003)Cost-effectiveness of brief cognitive behaviour therapyversus treatment as usual in recurrent deliberate self-harm: a rational decision making approach. PsychologicalMedicine, 33, 977^986.Fenwick, E.,Claxton, K. & Sculpher, M. (2001)Representing uncertainty: the role of cost-effectivenessacceptability curves. Health Economics, 10, 779^787.Fenwick, E.,O’Brien, B. & Briggs, A. (2004) Cost-effectiveness acceptability curves ^ facts, fallacies andfrequently asked questions. Health Economics, 13,405^415.Haddock,G., Barrowclough,C.,Tarrier, N., et al(2003) Cognitive^behavioural therapy andmotivationalintervention for schizophrenia and substance misuse: 18-month outcomes of a randomised controlled trial. BritishJournal of Psychiatry, 183, 418^426.Lothgren, M. & Zethraeus, N. (2000) Definition,interpretation and calculation of cost-effectivenessacceptability curves. Health Economics, 9, 623^630.AUTHOR’S PROOFELISABETH FENWICK, PhD,Department of Economics and Related Studies,University of York;SARAHBYFORD,MSc,Centre for the Economics of Mental Health, Institute of Psychiatry,De Crespigny Park,London,UKCorrespondence:Dr Elisabeth Fenwick,Department of Economics and Related Studies,University ofYork,Heslington,YorkYO10 5DD,UK.Tel: +44(0) 1904 321451; fax: +44(0) 1904 321402; e-mail:ealf100@york.ac.uk(First received 28 April 2004, final revision 14 September 2004, accepted 4 November 2004)For Evaluation Only.Copyright (c) by Foxit Software Company, 2004 - 2007Edited by Foxit PDF EditorMcCrone, P., Knapp, M., Proudfoot, J., et al (2004)Cost-effectiveness of computerised cognitive^behavioural therapy for anxiety and depression inprimary care: randomised controlled trial. British Journalof Psychiatry, 185, 55^62.Miller, P., Chilvers, C., Dewey, M., et al (2003)Counselling versus antidepressant therapy for thetreatment of mild to moderate depression in primarycare: economic analysis. International Journal ofTechnology Assessment in Health Care, 19, 80^90.O’Brien, B. & Briggs, A. (2002) Analysis ofuncertainty in health care cost-effectiveness studies: anintroduction to statistical issues and methods. StatisticalMethods in Medical Research, 11, 455^468.Scott, J., Palmer, S., Paykel, E., et al (2003) Use ofcognitive therapy for relapse prevention in chronicdepression: cost-effectiveness study. British Journal ofPsychiatry, 182, 221^227.Van Hout, B. A., Al, M. J.,Gordon,G. S., et al (1994)Costs, effects and C/E-ratios alongside a clinical trial.Health Economics, 3, 309^319.AUTHOR’S PROOFFor Evaluation Only.Copyright (c) by Foxit Software Company, 2004 - 2007Edited by Foxit PDF Editor

Analysis of Analysis of uncertaintyin health care cost-effectiveness studies: an uncertaintyin health care cost-effectiveness studies: an introduction to statistical issues and methods. introduction to statistical issues and methods.

B r i g g s ,A .&G r a y

Confidence intervals or Confidence intervals or surfaces? Uncertainty on the cost-effectiveness plane. surfaces? Uncertaintyonthe cost-effectiveness plane.

Copyright (c) by Foxit Software Company,

Cost- Costeffectiveness acceptabilitycurves ^ facts, fallacies and effectiveness acceptabilitycurves ^ facts, fallacies and frequently asked questions. frequently asked questions.

Costs, effects and C/E-ratios alongside a clinical trial. Costs, effects and C/E-ratios alongside a clinical trial.

Definition, Definition, interpretation and calculation of cost-effectiveness interpretation and calculation of cost-effectiveness acceptability curves. acceptabilitycurves.

Representing uncertainty: the role of cost-effectiveness Representing uncertainty: the role of cost-effectiveness acceptability curves. acceptabilitycurves.

S c o t t,J . ,P a l m e r ,S . ,P a y k e l

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