The Laryngoscope
C 2013 The American Laryngological,
V
Rhinological and Otological Society, Inc.
Contemporary Review
Health Economic Evaluation: Important Principles and Methodology
Luke Rudmik, MD; Michael Drummond, PhD
Objectives/Hypothesis: To discuss health economic evaluation and improve the understanding of common methodology.
Results: This article discusses the methodology for the following types of economic evaluations: cost-minimization, costeffectiveness, cost-utility, cost-benefit, and economic modeling. Topics include health-state utility measures, the qualityadjusted life year (QALY), uncertainty analysis, discounting, decision tree analysis, and Markov modeling.
Conclusion: Economic evaluation is the comparative analysis of alternative courses of action in terms of both their costs
and consequences. With increasing health care expenditure and limited resources, it is important for physicians to consider
the economic impact of their interventions. Understanding common methodology involved in health economic evaluation will
improve critical appraisal of the literature and optimize future economic evaluations.
Laryngoscope, 123:1341–1347, 2013
INTRODUCTION
In a time of increasing health care expenditure, there
is growing concern pertaining to the fiscal sustainability
of current health care systems across the globe. In 2009,
the United States spent an estimated $7,960 per capita on
heath care with approximately 17.4% of its Gross Domestic Product (GDP).1 Furthermore, according to a recent
Organization for Economic Co-operation and Development (OECD) report, health care spending in terms of
GDP is increasing on average of 4% per year.1 This data
emphasizes the importance of critically evaluating the
delivery of both current and future interventions in order
to ensure that resource allocation is cost-effective.
Due to significant technological advancements, clinicians’ are often faced with situations in which they need
to make an economic case to hospital or government
administrators in order to implement novel interventions.
Specifically, surgeons often encounter new devices with
higher costs, yet higher reported effectiveness. In this situation, a surgeon may want to perform an economic
evaluation to define the overall cost-effectiveness between
the new and old device in order to provide decision makers
with the necessary information for an efficient allocative
decision. Therefore, in addition to understanding clinical
From the Division of Otolaryngology–Head and Neck Surgery,
Department of Surgery (L.R.), University of Calgary, Calgary, Alberta,
Canada, and the Centre for Health Economics (M.D.), University of York,
York, United Kingdom
Editor’s Note: This Manuscript was accepted for publication on
November 26, 2012.
The authors have no funding, financial relationships, or conflicts
of interest to disclose
Send correspondence to Luke Rudmik, MD, Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, University of Calgary, Foothills Medical Centre, South Tower Suite 602, 1403–29th St. NW
T2N 2T9, Calgary, Alberta, Canada. E-mail: [email protected]
DOI: 10.1002/lary.23943
Laryngoscope 123: June 2013
outcomes research methodology, it is very relevant for clinicians to have an understanding of the various economic
evaluation methods in order to critically evaluate and perform high-quality economic investigations.
Economic evaluation (EE) is defined as the comparative analysis of alternative courses of action in terms of
both their costs and consequences.2 With the fundamental understanding that all health care resources are
scarce, in essence health EE is about evaluating
“choices” to determine the most cost-effective option for
allocation. In general, there are two approaches to
health EE: trial-based studies and modeling studies.
This article will discuss the different methods for economic evaluation and provide brief examples of each.
COST MINIMIZATION ANALYSIS
Cost minimization analysis refers to the simple comparison of cost between two interventions. This form of analysis
should only be used when the consequences between two
interventions are assumed to be the same; therefore, the
goal is to identify the intervention with the lowest cost. Most
experts believe that this simplistic analysis should not be
included under the title of economic evaluation.
An example of when a cost minimization approach
may be employed would be to compare the costs between
performing endoscopic sinus surgery (ESS) in a hospital
setting versus in a private surgical clinic. Since effectiveness outcomes should be the same regardless of the
operative setting in which the ESS is performed, a simple cost comparison could be applied.
COST-EFFECTIVENESS ANALYSIS
General
Cost-Effectiveness Analysis (CEA) is the evaluation
of the costs and consequences of alternative interventions
Rudmik and Drummond: Health Economic Evaluation
1341
TABLE I.
Advantages and Disadvantages of CEA.
Advantages
Disadvantages
1. Easier to produce since it uses common study clinical end-points
1. Inability to make interdisease comparisons
2. Requires less resources since the health outcome is typically already
being measured from the effectiveness component of the study
3. Tends to be easier for clinicians to interpret since it uses familiar
clinical end-points.
2. Cannot measure opportunity cost of
shifting resources
3. Challenge to define and justify the most
appropriate ‘effectiveness’ end-point
CEA 5 cost-effectiveness analysis.
using clinical outcomes in “natural units.” The natural
units can include a range of clinical end points such as,
life years gained, symptom-free days, complications
avoided, or cases diagnosed. The goal of CEA is to maximize societal health benefits while functioning within a
constrained budget.
Although there are several advantages of CEA
(Table I), the major disadvantage is the inability to provide
interdisease comparisons; therefore, it cannot measure the
opportunity cost of implementing one intervention over
another choice. Due to the inherent scarcity of health care
resources, the “opportunity cost” refers to the loss of health
benefits that would have been created if the resources were
used in another health care sector. The inability to measure opportunity cost creates a challenge for policy makers
to make appropriate decisions pertaining to efficient
resource allocation. Another disadvantage of CEA is defining the most important effectiveness end point to report.
The appropriate measure should reflect the objective of
performing the analysis, and it should consider units that
would improve policy decision-making. In circumstances
where there is an array of potential outcome measures, policy makers would prefer that the CEA report on several
different end points. Some authors label this form of
reporting a “cost-consequence” analysis.
Effectiveness data can be collected through the
standard methods of clinical outcomes research. The
quality of effectiveness data is imperative to a strong
economic evaluation. The quality of effectiveness study
design can be graded based on the Oxford levels of evidence.3 There will always be “uncertainty” associated
with both cost and consequence data collection, but the
goal is to try and minimize it, and then account for it
with statistical methods and sensitivity analyses.
eral other factors. Situations when policy makers may elect
to accept an intervention with a non-cost-effective ICER
include: 1) lack of an adequate alternative, 2) seriousness
of condition (e.g., tend to favor interventions that treat serious life-threatening conditions), 3) affordability from the
patient perspective, and 4) predefined ethical objectives.
Challenges of using the ICER include: 1) it gives no information on the size or scale of the intervention being
considered, and 2) there are statistical challenges in measuring differences between ICERs.
The cost-effectiveness plane (CEP) is a common
method of presenting cost-effectiveness outcomes (Fig. 1).4
The CEP plane demonstrates that any interventions
which fall in either quadrant II or IV are considered ”
dominant” and do not need ICERs to be calculated as
they are either more effective and less costly (II), or more
expensive and less effective (IV). Interventions falling in
quadrant II are typically always accepted, while those
falling in quadrant IV are typically rejected. Since interventions in quadrant I are more effective but more costly,
and those in quadrant III are less effective but less costly,
ICERs need to be calculated and compared. Once the
ICER between two interventions is calculated, the decision to accept the most cost-effective intervention (which
falls in either quadrants I and III) often depends on maximum ICER for which policy makers will accept. The
willingness to pay threshold for policy makers will vary
depending on health care objectives and budgets.
To overcome some of the disadvantages with using
the ICER, an increasingly popular method of presenting
Incremental Cost Effectiveness Ratio (ICER)
Cost-effectiveness comparisons should compare the
incremental costs and effects, meaning the additional
cost that one program imposes compared to the additional benefit it delivers. This should be expressed as
the “Incremental Cost-Effectiveness Ratio” (ICER),
which is calculated by dividing the incremental cost of
the new intervention by the incremental change in effectiveness. Just comparing the simple cost-effectiveness
ratios between two alternatives does not provide the
extra cost of adding the new alternative.
To get the best value for society’s money, policy makers want to implement interventions with the lowest ICER,
but policy decisions are complex and take into account sevLaryngoscope 123: June 2013
1342
Fig. 1. Cost-effectiveness plane.4 [Color figure can be viewed in
the online issue, which is available at wileyonlinelibrary.com.]
Rudmik and Drummond: Health Economic Evaluation
TABLE II.
Arguments for and Against Discounting “Effectiveness”
FOR Discounting Effectiveness Outcomes
AGAINST Discounting Effectiveness Outcomes
1. Leaving effects undiscounted while discounting costs can lead to inconsistencies in
reasoning and make future ICERs over-inflated.
1. Unlike costs, health effects cannot
be traded or invested
2. There is a potential for trading health in the future as people could theoretically trade
reductions in health status and services now, in return for healthy time in the future.
2. Discounting effects gives less weight to future
generations in favor for the present one.
3. By not discounting effects while discounting costs, it may lead policy makers to defer 3. Evidence suggests that individuals
discount health at a different rate than
decisions whenever they are encountered because the future cost is always lower than
monetary benefits.
the present while effects stay the same. (e.g., Why fund a surgical program today when
the effects are the same next year and the discounted costs will be lower)
ICER 5 incremental cost-effectiveness ratio.
results in CEA studies is converting the results to a Net
Monetary Benefit (NMB). The NMB calculation requires
defining the willingness to pay threshold for effectiveness,
and studies should use the country-specific value. The
willingness to pay threshold reflects how much policy
makers will spend to gain one extra unit of health outcome (i.e. effectiveness). The NMB is calculated using the
following formula: [Maximum WTP 3 Incremental Effectiveness]—Incremental
Cost.
There
are
several
advantages of using the NMB in CEA/CUA (cost utility
analysis) and authors should review the article by Stinnett and Mullahy5 for a more in-depth review of NMB.
In this method, specific ranges of probabilities are
applied to the specific parameter ranges, and a computer-generated random sample of potential outcomes is
produced. This will generate an empirical distribution of
cost-effectiveness. Results from a probabilistic SA are
typically presented using a cost-effectiveness acceptability curve (CEAC) which provides decision makers with
the probability of making a cost-effective decision at any
given willingness-to-pay ICER threshold. All data has
imperfections that results in uncertainty; therefore, SA
is imperative to determine how robust the results are in
the face of changing data.
Handling CEA Data Uncertainty
Discounting effectiveness
The most common method of handling data uncertainty is sensitivity analysis (SA) whereby the results of
the EE are studied after changing key variables for both
cost and effectiveness. There are several forms of SA for
EE, and a full review of this topic is beyond the scope of
this article. A strong SA is imperative to providing policy
makers with the necessary information to make informed
decisions; therefore, we will briefly discuss a few methods.
For an in-depth review of this topic, the reader is encouraged to review several excellent articles.6–10
Simple SA (i.e., one-way SA) is performed by varying single parameter estimates, one at a time, in order to
estimate the overall effect on the CEA results. Multiple
simple SAs can be combined into one graph and represented as a “Tornado diagram,” which provides a graphical
view of the effect of varying several individual parameters.
Simple SA is typically not accepted as the sole SA method
because handling the uncertainty of ICERs depends on the
combined variability of several factors.
Scenario SA is performed by creating a series of
scenarios with differing variables. It often includes a
“base case,” plus two scenarios at the extremes to enable
both a pessimistic view (high cost, low effectiveness) and
an optimistic view (low cost, high effectiveness) to be
presented.
Threshold SA refers to identifying the exact parameter values that create an ICER equal to the predefined
willingness to pay threshold. This is a helpful method to
determine what the maximum or minimum costs would
be in order to make an intervention cost-effective.
Probabilistic SA is considered the most robust and
comprehensive method of handling uncertainty in EE.
During economic evaluations, future costs are commonly discounted because of the impact of time
preference. Time preference refers to the advantage of
accruing costs in the future rather than today as a dollar
today is worth more than a dollar in the future. A positive rate of time preference for costs exists primarily due
to the consistent demonstration of positive economic
growth over time. Therefore, during economic evaluations, all future costs need to be discounted to accurately
reflect the lower cost compared to costs incurred today.
There is a debate whether effectiveness outcomes
should be discounted similarly to the way costs are discounted in the future. Furthermore, if interventions with
long-lasting effects are not discounted, then they will be
relatively more cost-effective in the future as cost is
reduced. Table II outlines the arguments for and against
discounting of effects during CEA and CUA. Despite significant controversy, current practice tends to discount both
costs and effects at the same rate. The National Institute
for Clinical Excellence (NICE)11 recommends that costs
and effects be discounted at 3.5%, while the World Health
Organization (WHO)12 recommends that costs and effects
be discounted at 3% for the base case, with a sensitivity
analysis using 0% for effects and 6% for costs.
Laryngoscope 123: June 2013
CEA STUDY EXAMPLE
A recent CEA study by Wilson et al.13 assessed the
cost-effectiveness of adenotonsillectomy when compared
with medical therapy for recurrent sore throats in
school-aged children. The effectiveness data was derived
from a United Kingdom (UK)-based randomized
Rudmik and Drummond: Health Economic Evaluation
1343
TABLE III.
Indications for Performing a CUA.
1. When quality of life is an important effectiveness outcome
2. When the intervention affects both morbidity and mortality
3. When the intervention had a broad range of
potential effectiveness outcomes, and you want to
produce one single general outcome for comparison
4. When you want to compare outcomes of your intervention
with other interventions already evaluated by CUA studies
5. When your local health care environment functions with a
budget, and you need data to assist in measuring the
opportunity cost
CUA 5 cost-utility analysis.
controlled trial, which randomized eligible patients to
receive either surgery or medical therapy. The main
effectiveness measure used for ICER calculation was episodes of sore throat, defined as a minimum of 3
consecutive days of sore throat. Over the 2-year period,
the mean number of sore throat episodes for the medical
therapy and surgical group were 11.4 versus 7.4, respectively. The estimated mean cost (in UK pounds) for the
medical and surgical groups were £463.22 versus
£1,402.15, respectively. The costs and effectiveness were
combined using an ICER (i.e., additional cost per sore
throat avoided), which demonstrated that when compared to medical therapy, the ICER of surgery was £261
per sore throat avoided. The authors concluded that in
UK school-aged children, tonsillectomy could save up to
eight sore throats at a reasonable cost; future prospective data collection is warranted.
nity cost of shifting scarce resources. Table III outlines
several indications of when to consider performing a
CUA for the EE.
CUA and CEA are similar in several domains;
therefore, we will not repeat a discussion on the ICER
and discounting of health effects. See Table IV for a comparison between CEA and CUA.
Measurement of patient preference
The two most widely used methods to measure
patient health-state preferences include: Time-trade off
(TTO), and Standard gamble (SG). These techniques
have formed the basis for defining patient preference
levels and were used to create the generic preferencebased systems such as the EQ-5D (Euro Quality of life 5
Dimensions), Health Utilities Index (HUI), and Shortform 6D (SF-6D). Measurement of patient preference
using either the TTO or SG techniques can be very timeconsuming and unrealistic in clinical research; therefore,
we will not discuss these techniques in this article.
Generic utility measures. To overcome the inherent challenges with using the TTO or SG techniques,
generic preference-based systems have been developed
to improve utility score determination. These generic
methods use a prescored multi-attribute health status
classification method in which scores are combined into
a common utility score. The three most common generic
preference-based measurement systems include: 1) HUI,
2) EQ-5D, and 3) SF-6D (Table V).
TABLE V.
Generic Utility Measurement Methods.
COST-UTILITY ANALYSIS
Cost-utility analysis is a form of EE that focuses on
measuring the patient’s preference for being in a particular health-state (a form of quality of life outcome). The
preference outcome is called a utility score, and is
recorded between 1 [perfect health] and 0 [death]. During CUA, the outcome is most commonly reported as the
cost per quality-adjusted life year (QALY). Less commonly used CUA outcomes include the disability
adjusted life year (DALY)14,15 and Healthy Years Equivalents (HYEs).16,17 The major advantage of CUA is that
outcomes can be compared across different disease
states; thus, they can be used to measure the opportu-
Generic
system
HUI 3
Available Formats
1. Self-administered
questionnaire
Speech
Ambulation
2. Interview form
Pain
EQ-5D
Self-administered
questionnaire
Mobility
2–3 minutes
Usual activity
Yes
Any “Natural Utility
Unit”
score
Same
Pain/discomfort
Anxiety/depression
No
Perform Incremental comparisons
for cost and effectiveness?
Yes
Yes
Ability for comparison across broad
range of interventions?
No
Yes
Ability to measure opportunity cost?
No
Yes
SF-6D
Self-administered
questionnaire
Physical function
3 minutes
Social function
Role participation
Bodily pain
CEA 5 cost-effectiveness analysis; CUA 5 cost-utility analysis.
1344
Dexterity
Emotion
Cognition
CUA
Effectiveness measure
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2–3 minutes
Self-care
Disease-specific effectiveness measure?
Cost measure
Vision
Time to
Completion
Hearing
TABLE IV.
Comparison of CUA and CEA.
CEA
Domains
Mental health
Vitality
EQ-5D 5 Euroquol 5 Dimensions; HUI 5 Health Utilities Index; SF6D 5 Short-form 6D.
Rudmik and Drummond: Health Economic Evaluation
Fig. 2. Quality-Adjusted Life Year (QALYs) gained from an
intervention.
The HUI currently has two systems, HUI2 and
HUI3.18,19 The HUI3 is the most commonly used since it
is more detailed and has population norms. Both are a
health questionnaires evaluating eight health domains,
with a preference scoring system based on standard
gamble utilities measured from the general public.
The EQ-5D contains five attributes: mobility, selfcare, usual activity, pain/discomfort, and anxiety/depression.20,21 Each attribute has three possible states which
provides 245 possible health states. Utility scores were
measured for each health state using the TTO technique.22
The SF-6D is based on the general QoL instrument
called the Short-Form 36 (SF-36) and Short form 12 (SF12).23 The SF-6D consists of six attributes which use
data from either 11 items from the SF-36 or eight items
from the SF-12. The utility scoring system was based on
standard gamble valuation technique performed in the
United Kingdom general population. Utility values on
249 potential health states were calculated.
The QALY
The QALY is defined as the product of the patient’s
life expectancy and the quality of life in those remaining
years.11 Although there continues to be debate over several controversies surrounding the QALY, it has become a
major outcome in CEA/CUA. Researchers should have an
understanding of the important concepts.
In the conventional approach to QALYs, the quality-adjustment weight for each health state is multiplied
by the time in the state (which may be discounted, as
discussed in the CEA section) and then summed to calculate the number of quality-adjusted life years. The
advantage of the QALY as a measure of health output is
that it can simultaneously capture gains from reduced
morbidity (quality gains) and reduced mortality (quantity gains), and integrate these into a single measure. A
simple example is displayed in Figure 2, in which outcomes are assumed to occur with certainty. Without the
health intervention, an individual’s health-related quality of life would deteriorate according to the lower curve,
and the individual would die at time death 1. With the
health intervention the individual would deteriorate
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more slowly, live longer, and die at time death 2. The
area between the two curves is the number of QALYs
gained by the intervention.
Despite several advantages of the QALY in EE,
there are several inherent limitations. First, it is challenging to truly define the cost per QALY for chronic
diseases. Patients may live with the disease for 30 to 50
years, and patients’ valuations of health states may not
be constant over time if they adapt to their disease.
Secondly, the cost/QALY threshold to make resource allocation decisions is poorly defined and varies
geographically. For example, the UK uses a threshold of
£20,000 to £30,000/QALY11 and North American studies
typically use $50,000/QALY or $100,000/QALY, although
there is no official threshold. Thirdly, CUA requires
extra resources to measure “utility” scores compared to
CEA for which the outcome measure is based on the
natural study clinical end point. Fourthly, there is a
debate whether or not future QALYs should be discounted. There is a belief that patients perform a
natural discount when reporting utility values, thus a
second analysis-related discount would produce a ‘double-discounting’ effect. Lastly, the QALY is thought to
disregard important equity concerns as it tends to favor
young and healthier populations. For example, elderly
patients have less life years expected; therefore, the
QALY may unfairly favor an intervention that affects a
teenager’s utility as they have more life years expected.
Furthermore, the QALY does not assess the impact of an
intervention on others who may be close to the patient.
For example, the impact of an intervention on the caregiver of an elderly person who gains clinical benefit from
the intervention is not typically included in the QALY
value; only the patient’s QALY value is included.
An example of a CUA performed in otolaryngology
may include comparing ESS versus medical therapy for
patients with chronic rhinosinusitis who failed initial
medical therapy. The effectiveness outcome is utility
level and can be measured using one of the three generic
preference based questionnaires (HUI, SF-6D, or EQ5D). Utility can then be converted to number of QALYs
based on the duration of time spent in the health state.
The ICER would represent the cost per additional QALY
provided.
In conclusion, although the use of QALYs in EE
will continue to be debated, it can provide valuable information to policy makers regarding resource allocation
such as measuring the opportunity cost of shifting
resources among different diseases. Thus, it can provide
information on both technical and allocative efficiency.
When quality of life is an important clinical outcome of
an intervention, the EE should consider incorporating
the QALY into economic outcomes.
COST-BENEFIT ANALYSIS
Cost-benefit analysis (CBA) is considered the most
comprehensive method for EE, and it is grounded in traditional welfare economics theory. In CBA, the
consequences of an intervention are valued in monetary
terms; therefore, it places money values on both inputs
Rudmik and Drummond: Health Economic Evaluation
1345
(costs) and outputs (benefits) of health care. Since outcomes are reported in monetary units, it is the best
method to inform allocation decisions. Policy makers can
assess the returns on investment from health compared
to investments in other areas of the economy, such as
education or national defense. The major challenge with
performing CBA in health care is converting health outcomes into monetary values. A thorough discussion on
CBA is beyond the scope of this article. Interested readers should review contingent valuation methodology to
convert health outcomes into monetary value.
Early stage glottic cancer may be treated with either radiation or surgery; the decision is often
dependent upon patient preference for weighing out the
advantages and disadvantages for each intervention. To
critically evaluate resource allocation, an otolaryngologist may want to perform a contingent valuation study
to define the willingness-to-pay (WTP) for both surgery
and radiation during the management of early glottic
cancer. This would convert the benefits from each intervention into a monetary value. Since overall survival
appears to be similar,24 the contingent valuation study
scenario would need to explicitly implement the differences in quality of life, potential risks, and voice outcomes.
Once the WTP is defined for each intervention (i.e.,
effectiveness converted into monetary value), a CBA can
be performed.
DECISION ANALYTIC MODELING
Decision modeling focuses on evaluating a specific
clinical decision or pathway. It incorporates data from
several sources and enters it into a framework that can
assist policy makers by identifying the most cost-effective
option. Decision modeling is different than trial-based
economic evaluation whereby data is not reliant on a single study. Since economic models intentionally simplify a
complex clinical scenario, critical components that must
be addressed when designing a decision modeling study
include: 1) identifying appropriate comparators, 2) including all relevant clinical data to define accurate
probabilities and expected effectiveness values, 3) define
how intermediate clinical end points can be extrapolated
into long-term outcomes, and 4) ensuring results can be
applicable to the decision-making context. Authors beginning an economic modeling study should refer to the
International Society for Pharmacoeconomics and Outcomes Research (ISPOR) task force recommendations on
decision analytic modeling for health care evaluation.25
This section will briefly discuss the two most common
modeling methods: Decision tree analysis and Markov
modeling.
Decision Tree Analysis
Decision tree analysis involves identifying the
expected costs and effects of following a patient through
clinical pathways resulting from a clinical decision. In the
decision tree, the “decision node” pertains to the square
box at the start of the tree and represents the decision
being evaluated in the model. The “chance node” pertains
to the range of possible clinical pathways that can result
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from making the intervention decision. Each branch from
the chance node has a probability assigned based on the
chance that a patient travels down that specific route. At
the end of the tree, each pathway has a combined probability, cost, and expected effect. These values are then
rolled back to the beginning to provide an overall costeffectiveness ratio for each decision in the model.
Although widely used, the major limitation of decision tree analysis is that it assumes the event occurs
within a single discrete time point, whereas in reality
further costs and benefits will accumulate over time as
the disease progresses. This is a major problem when
trying to use a decision tree model for chronic conditions. To overcome this limitation, Markov modeling was
developed and will be discussed in the next section.
A recent article by Rabalais et al. provides an
example of a decision tree analysis in otolaryngology.26
This study evaluated the cost-effectiveness of positron
emission tomography–computed tomography (PET-CT)
for the management of the neck following chemoradiation (CRT) for advanced oropharyngeal head and neck
squamous cell carcinoma (HNSCC). The decision tree
model evaluated four treatment strategies following
CRT: 1) neck dissection alone; 2) PET-CT performed
every 3 months; 3) neck dissection, then CT performed
every 3 months; and 4) neck dissection, then PET-CT
performed every 3 months. Their model demonstrated
that the use of PET-CT was the most cost-effective strategy for surveillance of the neck following CRT for
advanced HNSCC.
Markov Modeling
Markov modeling is a method whereby determining
cost-effectiveness is based on cycling patients through
“health states” rather than traveling down clinical
branches. Patients cycle through predefined health states
relevant to the disease being evaluated and accumulate
costs and effects based on which state they occupy.
The model cycles patients through the health states
for a defined number of cycles based on the duration of
disease. For each cycle, which may last 1 year, or a
shorter period for a disease that progresses rapidly,
patients cycle in and out of the different health states
based on predefined transition probabilities. For example, in a model with three health states (A, B, and C),
after each cycle a patient may either stay in their current state or move into one of the other two states,
depending on the transition probabilities. Therefore, at
the completion of each cycle, there is a cost and effect
for each health state. At the completion of all cycles (i.e.,
end of the model), the total cost-effectiveness ratio for
the intervention is calculated by summing up all the
weighted costs and effects for each individual cycle. Markov modeling therefore allows researchers to evaluate
decisions that result in economic outcomes over a long
period of time, rather than a single discrete time period.
A recent study by Sher et al. performed an excellent
modeling study evaluating the cost-effectiveness of CT
and PET-CT for determining the need for adjuvant neck
dissection in locally advanced HNSCC.27 Due to the
Rudmik and Drummond: Health Economic Evaluation
chronicity of cancer, the authors implemented a Markov
model of a standard patient with advanced oropharyngeal HNSCC. The model cycled patients through four
health states and two terminal states for 5 years: no
evidence of disease, local recurrence, nodal recurrence,
distant metastasis, death from HNSCC, or death from
other causes. Three post-CRT comparator treatment programs were evaluated and described: 1) perform surgical
neck dissection on all patients, 2) only perform neck dissection on patients with residual disease on CT, and 3)
only perform neck dissection on patients with residual
disease on PET-CT. The results from this study demonstrated that performing an adjuvant neck dissection
based on the presence of residual disease on PET-CT is a
dominant cost-effective strategy.
CONCLUSION
With the understanding that health care is a scarce
resource and everyone cannot have everything, policy
makers and health care workers must work to allocate
resources efficiently and equitably. Economic evaluation
is important to critically evaluate clinical interventions
and ensure that we are implementing the most costeffective management protocols. To improve critical appraisal of the literature and optimize future economic
evaluations, physicians should have a basic understanding of the principles and methodology involved in health
economic evaluation.
BIBLIOGRAPHY
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Methods for the Economic Evaluation of Health Care Programmes. 3rd
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3. Oxford Centre for Evidence-based Medicine (CEBM)-Levels of Evidence.
2009. http://www.cebm.net/index.aspx?o51025. Accessed November 5,
2012.
4. Black WC. The CE plane: a graphic representation of cost-effectiveness.
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Decis Making 2011;31:662–674.
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