1_3 Cost Effectiveness

7/27/2019 1_3 Cost Effectiveness

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1.3: Cost- and Comparative-Eectiveness 1.3.1

use o health care monies by ocusing on the clinical impact o

treatment strategies and the tradeos involved in selecting one

strategy over another.

Cost-Effectiveness Analysis CEA is a orm o economic-eciency analysis in which costs

are valued in monetary terms and health benets are valued

in natural units. Cost-eectiveness research and comparative-

eectiveness research are complementary in that the need to

establish clinical eectiveness is necessary or both. Cost-eec-

tiveness research has a more comprehensive goal o imparting

an economic context or dierences in clinical eectiveness.

A typical CEA is incremental, comparing some new health care

technology or strategy o interest with a relevant alternative.

Dierences in costs between strategies are measured in units o

currency. Clinical benets are expressed in nonmonetary terms,

such as lie-years gained or adverse events avoided.2

This analysis leads to various possible scenarios (Figure 1).

Compared with the standard o care, a new intervention can

be both cost-saving and more eective, in which case the

strategy is termed “dominant.” The new intervention can be

cost-increasing and less eective, in which case the strategy is

termed “dominated.” The most common situations are that thenew intervention is cost-increasing and is more eective, or an

intervention is less eective and cost-decreasing. In such cases, a

cost-eectiveness (CE) ratio is calculated. A CE ratio is derived as

ollows, where E represents the measure o eectiveness.2

CE ratio=CostNew – CostReference

ENew – EReference

Introduction: Scope of HealthCare Expenditures

An economic crisis was narrowly averted during US congres-

sional budgetary negotiations in August 2011. The negotiations

ocused on whether to raise the debt ceiling and/or cut govern-

ment expenditures. These negotiations, as well as the subse-

quent downgrading o the quality o the US government debt,

highlighted the limited resources our society has available to

spend on health care and the consequences o growth in health

care expenditures.

National health spending is estimated to have reached $2.6 tril-

lion in 2010. The share o the US gross domestic product spent

on health care is projected to increase rom 17.6% in 2009 to

19.8% by 2020.1 With limited resources, it becomes important

to consider the most ecient allocation o medical spending.

The study o this question is termed “medical economics.”

Fundamentals of Economic Assessment

There are three methods or the economic assessment o medical

interventions: 1) comparative-eectiveness analysis, 2) CEA (cost-

eectiveness analysis), and 3) cost minimization studies. For cost

minimization studies, the aim is to nd the least costly amongtherapies that are deemed equivalent. A major limitation o these

studies is the diculty in proving equivalence o strategies.

Comparative-Effectiveness Analysis Comparative-eectiveness analyses evaluate options or diag-

nosis and treatment in an attempt to answer important clinical

questions and guide medical practice. In an environment o

xed health care resources, this type o research may coun-

terbalance a desire to minimize costs and maximize ecient

Chapter 1: General Principles

1.3: Cost- and Comparative-EffectivenessDuane S. Pinto, MD, MPH, FACC, FSCAI

Consulting Fees/Honoraria: Terumo Medical, Rox Medical; Research Grants: Medicines Company

Matthew R. Reynolds, MD, MSc, FACC

Consulting Fees/Honoraria: St. Jude Medical, Sanof-Aventis, Biosense Webster, Inc.; Research Grants: Sanof-Aven-

tis, Biosense Webster, Inc., Edwards Liesciences

Learner Objectives

Upon completion o this module, the reader will be able to:

1. Explain key concepts in the interpretation o cost-eectiveness analyses (CEAs), including analytic perspective, time horizon,

incremental comparisons, and measures o uncertainty.

2. Identiy and dierentiate the ollowing CEA methodologies: trial-based, model-based, and hybrid.

3. Compare and contrast the economic implications o cost-minimization studies, cost-eectiveness studies, and cost-utility studies.

4. Recognize the importance o incremental cost-eectiveness ratios (iCERs) when evaluating new cardiovascular technology so

comparisons to existing technology can be made.



1.3.2 Chapter 1: General Principles

economic studies are an integral component o the evaluation

o any new medical treatment, and more or less explicit CE ratio

thresholds are known.4

Measures o clinical eectiveness vary or dierent disease

processes or interventions, making direct comparisons o health

conditions problematic. For example, the measurable eects o

a medication may be in avoiding impaired quality o lie ater a

stroke. A measurable benet o percutaneous coronary inter-

vention (PCI) or acute coronary syndromes may be in reducingrecurrent myocardial inarction. In CEA, measures o eect are

most easily interpretable when they lead to measurable changes

in the quantity or quality o lie.2

Cost-Utility AnalysisBecause many therapies are used primarily to improve quality o

lie rather than to increase longevity, it is necessary to have an

eectiveness measure that compares outcomes across interven-

tions which result in dierent health states. The quality-adjusted

lie-year (QALY) is a metric used to measure health benets. The

QALY incorporates both longevity and patient preerences or

the health states experienced. Cost-utility analyses are a subset

o CEA and utilize the QALY by adjusting or survival within

various imperect health states. The cost per QALY is a common

ratio reported in cost-utility analyses.

To develop the QALY or various disease states, a lie-year

gained in perect health is assigned a value o 1.0. Declining

weightings are assigned to various disease states, and 0.0 is

assigned or death. Patient preerences oer a weighting or

various disease states in between perect health and death and

are termed “utilities.” The utilities o various health states can

be developed in several ways. Each has limitations, and a major

problem with using QALYs or CEA stems rom inaccurate or

impractical assessment o patient preerences (i.e., utilities) or

various health states.

Methods o developing utilities include the ollowing:

Time-trade-o. Patients are asked to choose between remaining

in a current state o health or the remainder o lie, or being

restored to perect health but having a shorter lie. The duration

o time is varied until the person is indierent between the two

alternatives.5

Standard gamble. Patients are asked to choose between remain-

ing in a state o ill health or a period o time, or choosing a

medical intervention that has a chance o either restoring them

to perect health or killing them. The probabilities o the twooutcomes are varied until the patient is indierent between the

two outcomes.

Indirect assessment. Utilities can be assessed indirectly with the

use o health-related quality o lie indexes that have preas-

signed utility weights.5,6 For example, the EuroQol is a short

questionnaire that assesses ve health status domains: mobility,

sel-care, usual activities, pain/discomort, and anxiety/depres-

sion. Utility weights have been derived or the EuroQoL in both

Europe and the United States by using the standard gamble

method.5,7,8

I a therapy is more expensive, it must oer a greater clinical

benet to be cost-eective. In addition, cost-saving and cost-

eective are not synonymous terms, and it is possible or a less

expensive and slightly less eective strategy to be preerred on

health economic grounds, depending upon the acceptable CE

ratio. No single threshold exists or deciding whether or not

a CE ratio is acceptable and certainly the resources available

within a certain system apply.

Fearing the perception o “rationing” health care, US policy

makers presently are wary o making coverage decisions based

on cost considerations. Nevertheless, CE ratios o <$50,000 per

lie-year gained are generally considered attractive and ratios o

>$100,000 per lie-year gained are generally considered unat-

tractive. The $50,000 gure is historically linked to estimates

o the incremental cost per lie-year o hemodialysis. Because

Congress had guaranteed to und dialysis or all aected renal

ailure patients, the decision represents an implicit policy state-

ment by the US ederal government regarding its willingness to

pay to save a lie-year. Some have criticized this benchmark as

too low since it has not been updated in recent times.

There are certainly examples o the US Medicare program

agreeing to cover therapies with higher CE ratio thresholds.3 In

other nations, such as the United Kingdom and Australia, health

Figure 1The Cost-Effectiveness Plane

With some reerence strategy occupying the origin o the graph, a

cost-eectiveness study can plot the incremental costs (y-axis) and

benets (x-axis) o alternative strategies, relative to this reerence, in two-dimensional space. The area above the horizontal is cost-increasing,

and to the right of the vertical, clinically benecial. When a new

strategy adds both benets and costs (upper right-hand quadrant)

or reduces both (lower left-hand quadrant), a cost-eectiveness (CE)

ratio must be calculated to judge benets relative to costs.

CE = cost-eectiveness.

Reproduced with permission rom Cohen DJ, Reynolds MR.

Interpreting the results o cost-eectiveness studies. J Am Coll Cardiol

2008;52:2119-26.

The Cost-Effectiveness Plane

Dominated

CE Ratio

Cost-

Increasing

Cost-

Saving

CE Ratio

Dominant

Less Effective More Effective

Effectiveness

C o s t

-

+

-

+




the health o their beneciaries at the lowest costs. Most CEAs

do not deal with these individual incentives but instead evalu-

ate therapies rom the broader societal or health care systemperspective. As such, these analyses ocus on the aggregate so-

cietal costs and benets rom a strategy, rather than ocusing on

prots or losses or the hospital, physician, patient, or insurer.

Incremental Comparisons

As discussed earlier, CEAs describe the incremental benets o

two or more health strategies in light o the incremental costs.

An iCER is calculated with the presumption that each strategy

is compared with the next best alternative. ICERs refect the

marginal cost required to achieve a more avorable clinical out-

come over existing therapy, based on the economic concept o

“opportunity costs” (Table 1).9

Time HorizonThe time horizon o the analysis has an important eect on a

CEA. For example, i there is a short duration o ollow-up in a

clinical trial or an intervention that yields potentially long-last-

ing benets, the benets may be underestimated. This would

result in a spuriously high CE ratio. Furthermore, i there are di-

erences in the rate o benet accruing between the strategies

being compared, an inappropriate time horizon or comparison

may lead to misleading results. For example, consider a study

comparing the CE ratio o multivessel stenting versus coronary

artery bypass surgery. Assessing the CE ratio o PCI at 1 year is

somewhat limited by the act that most stents ail earlier, while

saphenous vein grat attrition is a phenomenon that occurs

later. This illustrates the act that a longer-term assessment o

costs may nd diering conclusions than early assessment.10

Uncertainty Because many assumptions are inherent to CEA, the potential

infuence o uncertain or assumed parameters should be evalu-

ated systematically through sensitivity analysis. This method

assesses the eect o assumed or uncertain parameters by recal-

culating results across an assumed range o plausible values or

the uncertain actor. In this way, assumptions that do not alter

Types of Economic Analyses

Three types o health economic studies have

been perormed: 1) economic analysis per-

ormed alongside a randomized clinical trial,

2) model-based analysis based on best avail-

able evidence, and 3) hybrid studies where

observed patient-level trial data are used or

some portion o the analysis and then used

to extrapolate beyond the time horizon o thetrial itsel.

Clinical Trial-Based Analyses Clinical trials aord the opportunity to per-

orm CEA with accurate data collection, and

randomization reduces the chances o bias

and conounding. These types o analyses are

limited by the selected populations enrolled in

what is usually a single clinical trial. Additional

limitations include the expense involved in

collecting these data, and the limited time span o these trials

in which downstream clinical and economic costs may not be

identied.

Disease Simulation Models When clinical trials are not practical or refective o the popula-

tion o interest, other analyses are perormed that are com-

pletely based on disease simulation models. Using a wide variety

o data sources, models are constructed to predict the likelihood

o various outcomes. Multiple strategies can be assessed, and

because various sources o data are used, there is not overreli-

ance on one population or study. Unortunately, the quality o

the models and conclusions are undamentally dependent on

the quality o the data used to construct the models. Quality is

also dependent on the simpliying assumptions that are neces-sary when constructing these models. Results may thereore be

uncertain. However, the results may be useul or generating

hypotheses.

Hybrid Studies Some studies incorporate elements o both modeling and data

collection rom clinical trials. By deriving models rom clinical

trial data and then projecting results into a longer time horizon,

the issue o truncated ollow-up is partially addressed. However,

the issues o incomplete or inaccurate modeling remain.2

Interpretation of Cost-Effectiveness Analyses

Analytic Perspective One consideration when interpreting a CEA is the analytic

perspective. Each member o the health care delivery team (e.g.,

consumer, insurer, physician, hospital) has dierent incentives,

constraints and obligations. As such, each may have dier-

ent preerences or one therapy over another. Because most

patients with health insurance do not have large out-o-pocket

expenses, there is a desire to have the maximal health benet,

regardless o cost. Hospitals have an incentive to maximize

return or any given episode o care. Insurers seek to maximize

Table 1Example of Incremental Cost-Effectiveness Ratio

iCER = incremental cost-eectiveness ratio.

Example of Incremental Cost-Effectiveness Ratio

Strategy Total CostIncrease in Incremental Incremental

iCEREffect Effect Cost

Standard $50,000 3.0

Alternative A $400,000 4.0 1.0 $350,000 $350,000

Alternative B $550,000 4.8 0.8 $150,000 $187,500

Alternative C $750,000 9.0 4.2 $200,000 $47,619




the overall results can be identied. It is also

possible to identiy the threshold values or im-

portant parameters that will cause one strat-

egy to become preerred over another. Rather

than vary one parameter at a time, complex

methods (e.g., Monte Carlo simulations) can

be used to simultaneously vary multiple actors

at once so that the overall uncertainty o a

model can be evaluated.

For trial-based analyses, specialized methods

are utilized to estimate the uncertainty around

estimates or CE ratios. This is because stan-

dard statistical measures o variance cannot be

applied to an iCER. One method, bootstrap-

ping, involves creating a “dummy” data set by

resampling with replacement (i.e., randomly

selecting one patient at a time) rom the origi-

nal data set and repeating this random patient

selection until the dummy data set reaches

the same size as the original. The CE ratio is

then recalculated rom the dummy data set,

and the entire process is repeated many times

(e.g., 1,000 times).

The average CE ratio, calculated over many

bootstrap iterations, should approximate the

original estimate rom the trial data. However,

the variability within the original study results

in a “cloud” o possible outcomes with each

bootstrap iteration. Acceptability curves can

be constructed where a probability that an

intervention would be attractive at various CE

ratios can be visualized (Figure 2).

CEA aords the ability to evaluate the eco-

nomic consequences o clinical decisions that

go beyond assessment o clinical outcomes

alone. Unortunately, CEA cannot be read-

ily applied to an individual patient. It should

only be used as one o many tools available to

caregivers and policy makers when making decisions.

Examples of Cost-Effectiveness Analyses in Cardiology

The SIRIUS Trial

The SIRIUS (Sirolimus-Eluting Balloon Expandable Stent in theTreatment o Patients With De Novo Native Coronary Artery

Lesions) trial was the rst randomized trial to compare a drug-

eluting (i.e., sirolimus) coronary stent with a bare-metal stent.

The cost-eectiveness study by Cohen, et al. is a good example

o a trial-based CEA.11 All data on clinical eectiveness (e.g.,

survival, repeat revascularization procedure rates), medical

resource utilization, and costs were collected and analyzed dur-

ing the 1-year ollow-up period o the trial. This approach was

reasonable because the choice o stent did not impact survival.

Also, the time-course o restenosis, the main outcome improved

by the drug-eluting stent, is known to be generally well less

than 1 year. The economic analysis o the SIRIUS trial ound that

sirolimus-eluting stents slightly increased overall costs but great-

ly reduced repeat revascularization procedures. This resulted in

iCERs o $1,650 per repeat revascularization procedure avoided

and $27,540 per quality-adjusted year o lie gained.

The SCD-HeFT Trial The SCD-HeFT (Sudden Cardiac Death in Heart Failure Trial)

compared amiodarone, placebo, and implantable cardioverter-

debrillators (ICDs) or the prevention o sudden death in

patients with reduced let ventricular ejection ractions.12 ICDs

decreased all-cause mortality compared with placebo, but

amiodarone did not. In the CEA published by Mark, et al., the

key comparison was thereore between the ICD and placebo

groups.13 An ICD is an example o an intervention with a high

up-ront cost and potential long-term benet. A trial-based CEA

or such an intervention would tend to overestimate the iCER by

Figure 2Bootstrap Analysis of Index Hospital Stay Costs

Perormance o 1,000 bootstrap replicates indicates a 94.6% probability o cost savings with

bivalirudin monotherapy compared with heparin (unractionated or low molecular weight

heparin) with upstream glycoprotein IIb/IIIa receptor inhibition (GPI) (yellow line) and a 68.3%

probability o lower cost with bivalirudin monotherapy compared with heparin (unractionated

or low molecular weight heparin) and catheterization laboratory (cath lab)-initiated GPI (red

line).

Reproduced with permission rom Pinto DS, Stone GW, Shi C, et al. Economic evaluation

o bivalirudin with or without glycoprotein IIb/IIIa inhibition versus heparin with routine

glycoprotein IIb/IIIa inhibition or early invasive management o acute coronary syndromes. J Am

Coll Cardiol 2008;52:1758-68.

Bootstrap Analysis of Index Hospital Stay Costs

Bivalirudin vs.

Heparin + Upstream GPI

Bivalirudin vs.

Heparin + Cath Lab GPI

94.6%

68.3%

C u m u l a t i v e P r o b a b i l i t y

0%

20%

40%

60%

80%

100%

-$2,500 -$1,500 -$500 $500 $1,500




excellent precision but may have insucient time horizons.

Models may suer rom uncertainty but can provide

insights in cases where clinical trials are not easible or have

not been completed.

• An iCER is the ratio o the incremental costs (in units o

currency) o one strategy versus another, divided by the

incremental benets. The benets are dened as some

clinical outcome o value (e.g., lie-years gained, adverse

events avoided).

• QALYs are a commonly used eectiveness measure in

health economics and are obtained by multiplying lie

expectancy by weights ranging rom 0 to 1. The weights

represent society’s preerence or some health states

over others. These weights are called utilities and can be

measured in several dierent ways.

References

1. Keehan SP, Sisko AM, Truer CJ, et al. National health spending

projections through 2020: economic recovery and reorm drive

aster spending growth. Health A (Millwood) 2011;30:1594-605.

2. Cohen DJ, Reynolds MR. Interpreting the results o cost-eective-ness studies. J Am Coll Cardiol 2008;52:2119-26.

3. Chambers JD, Neumann PJ, Buxton MJ. Does Medicare have

an implicit cost-eectiveness threshold? Med Decis Making2010;30:E14-27.

4. Lee CP, Chertow GM, Zenios SA. An empiric estimate o the value

o lie: updating the renal dialysis cost-eectiveness standard.

Value Health 2009;12:80-7.

5. Mark DB, Hlatky MA. Medical economics and the assessment o value incardiovascular medicine: part I. Circulation 2002;106:516-

520.

6. Mortimer D, Segal L. Comparing the incomparable? A system-

atic review o competing techniques or converting descriptivemeasures o health status into QALY-weights. Med Decis Making

2008;28:66-89.

7. Dolan P. Modeling valuations or EuroQol health states. Med Care1997;35:1095-108.

8. Shaw JW, Johnson JA, Coons SJ. US valuation o the EQ-5D health

states: development and testing o the D1 valuation model. Med

Care 2005;43:203-20.

9. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-Eectiveness inHealth and Medicine. New York: Oxord University Press;1996.

10. Cohen DJ, Lavelle TA, Van Hout B, et al. Economic outcomes o

percutaneous coronary intervention with drug-eluting stents versusbypass surgery or patients with let main or three-vessel coronary

artery disease: One-year results rom the SYNTAX trial. Catheter

Cardiovasc Interv 2011;May 3:[Epub ahead o print].

11. Cohen DJ, Bakhai A, Shi C. Cost-eectiveness o sirolimus-eluting stents or treatment o complex coronary stenoses: results rom

the Sirolimus-Eluting Balloon Expandable Stent in the Treatment o

Patients With De Novo Native Coronary Artery Lesions (SIRIUS) trial.Circulation 2004;110:508-14.

12. Bardy GH, Lee KL, Mark DB, et al. Amiodarone or an implantable

cardioverter-defbrillator or congestive heart ailure. N Engl J Med

2005;352:225-37.

ailing to capture the ull benets o the ICD over time. Mark,

et al. thereore perormed a hybrid analysis, in which empiri-

cal data rom the in-trial period were combined with model-

based extrapolations o lietime costs and survival. The authors

reported iCERs or the ICD compared with the placebo group o

$38,389 per lie-year saved and $41,530 per QALY saved.

Cost-Effectiveness of Atrial Fibrillation AblationSeveral small randomized trials have shown that catheter abla-

tion is more eective than antiarrhythmic drugs at maintaining

sinus rhythm in patients with atrial brillation who have ailed at

least one prior drug. None o these trials have collected the nec-

essary data to perorm a CEA. Even i they had, the data would

be limited by high crossover rates in the trials and by relatively

short ollow-up durations. To overcome these limitations, Reyn-

olds, et al. perormed a CEA using a Markov model.14

A Markov model is a means o estimating the prognosis when

the clinical problem is associated with ongoing risk. In a Markov

mode, a nite number o health states (i.e., Markov states) as-

sociated with the disease are dened. Each state is associated

with a actor representing the quality o lie as compared with

perect health. The probabilities o transitioning between dier-

ent health states, as well as the costs and quality o lie o each

state, are either taken rom published literature or derived. The

time horizon or study is divided into increments (i.e., Markov

cycles), and the Markov cycle is repeated over and over. The

time spent in the various Markov states, each associated with an

incremental utility, is estimated. The utility accumulated over the

Markov process is the product o the incremental utility o the

Markov state and the number o cycles spent in that state.15,16 In

this case, the authors reported that catheter ablation increased

both 5-year costs and 5-year quality-adjusted survival. This

resulted in an iCER o $51,000 per QALY.

Future Directions

Economic and political realities are currently placing downward

pressure on the costs o health care, and this will continue in the

uture. Cardiovascular medicine has made very large contribu-

tions to the population’s health, longevity, and quality o lie.

Cost-eectiveness studies have ound that many interventions,

even some expensive ones, provide excellent benets at a rea-

sonable price. It will be necessary to careully evaluate the value

o new clinical approaches to diagnosing and treating cardiovas-

cular disease as they emerge.

Key Points

• The United States spends a larger proportion o its wealth

on health care than any other developed nation, and

the costs o health care are growing at an unsustainable

rate. Cost-eectiveness analyses attempt to help us

better understand both the clinical and economic value o

alternative clinical strategies.

• Cost-eectiveness analyses can be trial-based, model-

based, or a hybrid o the two. Trial-based analyses have




13. Mark DB, Nelson CL, Anstrom KJ, et al. Cost-eectiveness o

defbrillator therapy or amiodarone in chronic stable heart ailure:

results rom the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). Circulation 2006;114:135-42.

14. Reynolds MR, Zimetbaum P, Josephson ME, Ellis E, Danilov T,

Cohen DJ. Cost-eectiveness o radiorequency catheter ablation

compared with antiarrhythmic drug therapy or paroxysmal atrial fbrillation. Circ Arrhythm Electrophysiol 2009;2:362-9.

15. Sonnenberg FA, Beck JR. Markov models in medical decision mak-

ing: a practical guide. Med Decis Making 1993;13:322-38.

16. Briggs A, Sculpher M. An introduction to Markov modelling or economic evaluation. Pharmacoeconomics 1998;13:397-409.

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