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An introduction to cost-effectiveness andcost–benefit analysis of pharmacogenomics

Kathryn A Phillips†1, David Veenstra2, Stephanie Van Bebber3 & Julie Sakowski3

†Author for correspondence1School of Pharmacy, Institute for Health Policy Studies, and UCSF Comprehensive Cancer Center, University of California, San Francisco, 3333 California Street #420 Box 0613, San Francisco, CA 94143, USATel: +1 415 502 8271; Fax: +1 415 502 0792; E-mail: kathryn@itsa.ucsf.edu2Department of Pharmacy Health Sciences, University of Washington, Box 357630, Seattle, WA 98195, USA3School of Pharmacy, University of California, San Francisco, 3333 California Street #420 Box 0613, San Francisco, CA 94143, USA

Ashley Publications Ltd

Keywords: cost-effectiveness analysis, economic evaluation, pharmacogenetics, pharmacogenomics

www.ashley-pub.com

2003 © Ashley Publications Ltd

Methods of economic evaluation, especially cost-effectiveness analysis and cost–benefit analysis, are widely used to examine new healthcare technologies. However, few economic evaluations of pharmacogenomics have been conducted, and pharmacogenomic researchers may be unfamiliar with how to review or conduct these analyses. This review provides an overview of the methods of economic evaluation and examples of where they have been applied to pharmacogenomics. We discuss the steps in conducting a cost-effectiveness or cost–benefit analysis, demonstrating these steps using specific examples from the pharmacogenomics literature.

IntroductionMethods of evaluating the costs and benefits ofhealthcare have become increasingly importantdue to the rising costs of healthcare, and thenumber of economic evaluations of healthcarehas increased dramatically [1-3]. Economic evalu-ations assess trade-offs of scarce resources thatresult from utilization of health technologies bycomparing competing healthcare alternatives.Cost-effectiveness analysis (CEA) and cost–ben-efit analysis (CBA) in particular provide deci-sion-makers with a framework whereby they canmake decisions regarding healthcare provision,insurance reimbursement, and drug develop-ment given a fixed budget and competingchoices. By comparing the relative value of inter-ventions, CEA provides a way to illuminate thelost health benefits – longer life or decreasedmorbidity – of not selecting the next-best alter-native [4,5]. Several articles have noted that phar-macogenomics has the potential to influence notonly health outcomes but also the delivery andcost of healthcare. However, there have been fewstudies to empirically evaluate this impact [6-9].To ensure that pharmacogenomic technologiescan be implemented in an efficient and cost-effective manner, it is critical that the methods ofeconomic evaluation in healthcare be applied topharmacogenomics [10-13].

The objectives of this review are to:• Provide an overview of the methods of eco-

nomic evaluation in healthcare, particularlycost-effectiveness and CBA, and how theyapply to pharmacogenomics.

• Discuss the steps in conducting economicevaluations, using specific examples from asystematic review of the pharmacogenomicsliterature.

Several guides to conducting economic evalua-tion and CEA have been developed [4,5,14-16]. Wepreviously developed a framework for evaluatingthe potential cost-effectiveness of pharmacoge-nomic technologies [3]. This study expands ourprevious work to include a more detailed reviewof the methods of economic evaluation asapplied to pharmacogenomics by discussing thespecific steps in conducting CEA and CBA ofpharmacogenomics. We use specific examplesidentified from a systematic search of the litera-ture. The methodology used for our systematicsearch is discussed in detail elsewhere [17].

Our literature search identified six studies thatexamined the cost-effectiveness of pharmacoge-nomics [18-23]. We used a broad definition ofpharmacogenomics that included the use ofgenetic information to target drug therapiesbased on either inherited (host) or acquired (e.g.,tumor or viral) mutations. Two studies were ongenotyping for deep vein thrombosis (DVT) andtwo of the studies evaluated genotyping hepatitisC virus (HCV) compared to an array of alterna-tive pretreatment strategies to determine subse-quent drug treatment. Another study evaluatedgenotyping HIV-1 to identify variants with drugresistance [18] and the final study conducted aCEA of screening for thiopurine S-methyltran-sterase polymorphism (TPMT) prior to treatingpatients suffering from rheumatological condi-tions with azathioprine [23]. Four of the six stud-ies found genotyping to be relatively cost-effective [18,21-23], while two studies found it tobe less cost-effective than other options [19,20].

Methods of economic evaluationMethods of economic evaluation provide aquantitative framework for evaluating the

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Table 1. Methods of

Study design D

Cost-minimization Uc

Cost-consequences Vesawoim

Cost–benefit Vin

Cost-effectiveness Vtcvoinisp

Cost–utility Vtcvy

complex and often conflicting factors involved inthe evaluation of healthcare technologies [3].Importantly, it helps ensure that all costs andbenefits resulting from a healthcare interventionhave been properly evaluated. There are severaltypes of economic evaluation that are used inhealthcare: cost-minimization analysis, cost-con-sequences analysis, CBA, CEA, and cost–utilityanalysis (CUA) (Table 1). These methods varyprimarily in the way they measure health out-comes, for example, in monetary terms, naturalunits such as life-years gained or lives saved, orquality of life adjusted life expectancy, or in thecase of cost-minimization analysis the assump-tion that health outcomes are identical.

Although CEA is a specific type of economicevaluation that measures cost in relationship totangible outcomes gained, such as life-yearssaved, the term is commonly used (sometimesmistakenly) to refer to all types of economic eval-uation in healthcare. CUA is a specific type ofCEA, which has become widely accepted inhealthcare because it measures benefits inpatient-oriented terms (quality of life) and per-mits comparison between different interventions

by standardizing the denominator. CBA valuesboth costs and effects (benefits) in monetaryterms, presented either in the form of a ratio ornet benefits. To illustrate, if we conducted a cost-effectiveness study comparing genotyping versusnot genotyping prior to the administration of adrug for individuals with a known mutation A,our result might be US$10,000 per life-yearsaved. On the other hand, a CUA might obtain aresult of US$9,000 per quality-adjusted life yearsand a CBA might obtain a result of net benefitsof US$500.

In this study, we focus on the methods rele-vant to CEA, CUA, and CBA because these arethe most commonly used and acceptedapproaches for evaluating healthcare technolo-gies. However, the steps we discuss apply gener-ally to all forms of economic evaluation. For thepurposes of this review, we do not distinguishbetween the use of the terms ‘pharmacogenetics’and ‘pharmacogenomics’.

Steps in conducting economic analysesThe US Panel on Cost-Effectiveness in Health-care provided general recommendations for

economic evaluation.

escription Strengths Weaknesses

sed when effects are identical; ompares costs only

Easy to perform Only useful if effectiveness assumed to be the same

alues costs and benefits of ach comparison program eparately and often with an rray of outcome measures ithout comparing the benefits r indicating their relative portance

Data presented in straightforward fashion

A ratio is not calculated, thus making comparisons of health interventions difficult

alues all costs and all benefits monetary terms

Good theoretical foundation can be used within healthcare and across sectors of the economy

Less commonly accepted by healthcare decision makersEvaluation of benefits methodologically challenging

alues all costs in monetary erms while effects of omparison programs are alued with a relevant health utcome, such as, ‘mmHg drop diastolic blood pressure’ that common to all comparison rograms

Relevant for cliniciansEasily understandable

Cannot compare interventions across disease areas

alues all costs in monetary erms while effects of omparison programs are alued with quality adjusted life ears (QALYs)

Incorporates quality of life by adjusting changes in life-years for differences in health benefits/effectsComparable across disease areas and interventions

Quality of life requires evaluation of patient preferencesCan be difficult to interpret

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Box 1. Key steps in

Step 1: Define researc• Develop concise, clea• Conduct literature rev• Define current and po

(societal, payer, insurethe relevant disease arelevant future effects

Step 2: Assess costs, b• Determine data sourc• Develop estimates for• Adjust costs and bene• Describe the conceptu

Step 3: Calculate and • Calculate and present• Conduct sensitivity an

inputs and model

Step 4: Interpret resul

performing cost-effectiveness analyses [4]. Thesegeneral recommendations, together with otherkey guides (e.g., [5,14-16]), provide the frame-work below.

Box 1 summarizes the steps to conduct an eco-nomic evaluation. Each step includes severalillustrative points, which may be relevant to spe-cific analyses. We illustrate these steps usingexamples gathered during a systematic review ofthe literature of economic evaluations ofpharmacogenomics [17].

Step 1: Define research question and study frameworka) Develop concise, clear, and answerable research questionIn general, defining the research questioninvolves steps that are not unique to pharma-cogenomics studies and is similar with respectto demonstrating any question’s significance. Adescription of the basic problem underscoresthe pharmacogenomic strategy’s significanceand describes the relevant alternative drugs ortherapies. This step is particularly important topharmacogenomics because it includes theprevalence of the disease and/or mutation, andthe known morbidity and mortality, as well asdetailing the current known costs of diseaseand/or mutation effects. It has been noted thatmost single-gene mutations are uncommon,most mutations do not have a phenotypiceffect, and mutations may contribute to but not

necessarily cause diseases [24]; thus creatingpotential barriers to developing the pharmacog-enomic research question.

b) Conduct literature review to determine what is currently knownBecause the field is rapidly changing, locatingrelevant literature on economic evaluations ofpharmacogenomics is particularly important butalso problematic. For example, PubMed does notinclude a medical subject heading (MeSH) termfor pharmacogenomics, and thus the term phar-macogenetics has to be used. In addition, theavailable MeSH term for economic evaluations iscost–benefit analysis. Specific types of economicevaluations (e.g., cost-effectiveness analyses) areincluded underneath this broader term, with theresult that it is more difficult to identify specifictypes of studies.

Another major barrier is that it does not appearthat all relevant studies can be located using theMeSH term pharmacogenomics. Thus, in ourcomprehensive search, of cost-effectiveness evalu-ation of pharmacogenomics, we included theMeSH headings drug resistance/drug effects,drug resistance/genetics, genotype, and mutationas well as MeSH terms for the most relevantexamples of genetic variations that effect drugtherapy known to us at the time of our review. Insummary, comprehensively locating the literatureon economic evaluations of pharmacogenomicsrequires multiple search strategies using bothMeSH subject terms and keywords.

c) Define current and potential interventions; state perspective for analysis (societal, payer, insurer etc.); define population (including prevalence of the relevant disease and mutation); define time horizon to include all relevant future effects of interventionInitial decisions, such as the interventions to beexamined, study perspective, and time horizon,are important for economic evaluations of phar-macogenomics to clearly frame and define thescope of the study. The study perspective is thedetermination of what group affected by theintervention will be considered in the evaluation.What will be considered relevant costs and bene-fits for evaluation can vary greatly according tothe perspective chosen. Study perspectiveoptions include societal, insurer, payer, industry,and government, and are particularly importantwith respect to the costs that will be included inthe analysis. For example, from the perspective

conducting an economic evaluation.

h question and study frameworkr, and answerable research questioniew to determine what is currently knowntential interventions; state perspective for analysis r etc.); define population (including prevalence of nd mutation); define time horizon to include all of intervention

enefits/effects, and outcomeses and type of model to be used costs, benefits/effects, and outcomesfits/effects for time (discounting)al model using an event pathway (decision tree)

present results primary resultsalyses to assess the impact of changing the data

ts and place into context

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of the pharmacogenomics company, patient timecosts such as lost wages may not be relevantwhile from the societal perspective the wages lostdue to receiving healthcare or due to illness maybe substantial. The time horizon is the period forwhich costs and benefits/effects will be collectedfor the analysis.

Such decisions will depend on the nature andpurpose of the study but should be explicit at thestart of the study to ensure that the appropriatemodel and data are collected. When the purposeof the study is to examine the broad allocation ofhealth resources, and when comparability toother studies is important, the US Panel onCost-Effectiveness in Health and Medicinedeveloped guidelines for a typical or ‘referencecase’ analysis [4]. For reference case analyses, therecommendations are to:

• use a societal perspective• estimate costs and benefits or effects over a rel-

evant long-run time horizon• use as a comparison program/treatment the

current standard of care and/or where appro-priate a ‘do nothing’ approach

• use quality-adjusted life-years as the outcomemeasure

• use a discount rate of 3%

Of particular interest to pharmacogenomics isthe specific population to be evaluated. First, asnoted by Veenstra and colleagues [3], the preva-lence of the gene mutation can greatly affect theresulting cost-effectiveness and net benefits ofrelevant interventions. Second, in the case ofinherited mutations, a positive finding for amutation in the proband may suggest testing forfamily members. The costs and benefits of suchtesting will differ from those of the proband butshould be considered.

Underscored in the introduction of thisreview is the critical step of determining the pro-posed intervention’s effectiveness relative to analternative. When conducting cost-effectivenessanalyses, the proposed intervention is alwayscompared to a comparison program. For exam-ple, a genotyping strategy prior to drug therapymight be compared to a phenotyping strategyinstead of a ‘do nothing’ strategy. The choice ofcomparison program is very important in deter-mining the validity and usefulness of the CEAresults. The program chosen should be the onemost likely to be replaced by the new program.When clinical trial data are not available com-paring the proposed and current program, careneeds to be taken to evaluate the use of indirect

comparisons. In an economic evaluation of phar-macogenomics, there are essentially two effec-tiveness components: first, the effectiveness ofthe genetic test to identify the mutation carrier,and second, the effectiveness of the subsequentchanges in drug therapy for the mutation carri-ers. As discussed further in the next section,these are critical aspects.

Example from systematic literature reviewWeinstein et al. conducted a study to assess thecost-effectiveness of genotypic resistance testingfor patients acquiring drug resistance throughfailed treatment (secondary resistance) and thoseinfected with resistant virus (primary resistance)[18]. This study carefully defined the two researchquestions (secondary and primary resistance), theinterventions (genotypic resistance testing andclinical judgment versus clinical judgmentalone), perspective (societal), population (HIV-infected patients in the US with baseline CD4counts of 0.250 x 109 cells/l), and time horizon(lifetime). Further, the authors specifically notedthat they followed the ‘reference case’ recommen-dations to ensure comparability to other analyses.

Step 2: Assess costs, benefits/effects, and outcomesa) Determine data sources and type of model to be usedData can be obtained from a variety of sourcesincluding primary data collection as part of aclinical trial and secondary data obtained fromthe literature. Most economic evaluations alsoemploy mathematical or simulation modeling toprovide estimates for incomplete or unavailabledata. Modeling is acceptable in cases where nei-ther primary nor secondary data are available toestimate the effectiveness of the intervention.Models, however, should be based on realisticassumptions about the data and when possible,validated against other data. There are two maingroups of models: decision-analytic models andepidemiological models. Decision-analyticalmodels are most commonly used in economicevaluations, and would include models such asdecision-trees and state-transition models suchas Markov models. Epidemiological models havebeen used to model chronic diseases such asheart disease [25].

b) Develop estimates for costs, benefits/effects, and outcomesThe next step is to assess the data on costs, bene-fits/effects, and outcomes in order to incorporate

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them into the analysis. All major resources usedin the intervention should be included in theanalyses and should include both direct and indi-rect (‘productivity’) costs. The term ‘costs’ refersto the value of resource inputs (as compared to‘charges’, which is the amount charged to payers).Direct costs are all costs where funds are paid outas a result of the intervention or its consequences.Indirect costs are opportunities forgone or impo-sitions that are a result of the intervention such astime costs. For a CEA of pharmacogenomics thatcompares genotyping versus not genotyping priorto initiating drug therapy, costs might includecosts of the genetic test and of subsequent drugtherapy as well as costs of genetic counseling(direct costs) and patient time costs to attendtesting, counseling, and/or follow-up (indirectcosts). The major cost components relevant incost-effectiveness analyses are typically cost ofhealthcare services, costs of patient time, costsassociated with care-giving, other costs associatedwith illness, and costs associated with the non-health impacts of the intervention.

Effects are measured in a variety of ways. Acommonly reported approach is quality-adjustedlife years (QALYs). QALYs incorporate the con-cept that alternative health interventions do notprolong a year of life equivalently. Using QALYsin economic evaluations allows for comparinghealth states associated with similar life-years butdifferent morbidity. A second advantage ofQALYs is that the relative value of programsacross disease can be compared because the effec-tiveness outcome measure is the same.

QALYs are calculated by multiplying a ‘qual-ity’ number between 0 (worst imaginable health)and 1.0 (ideal health) for various health states bythe life-years saved by the intervention. In gen-eral, the utility numbers represent the satisfac-tion or happiness for different health statesassociated with either the disease and/or thedrug. For example, suppose a drug to preventdisease X is given to an individual with mutationA but that drug X has as a side effect, daily nau-sea. If the individual gains one life-year as a resultof the drug but also suffers from nausea, thehealth state, daily nausea, might be valued lessthan ideal health and thus the QALY would besome value < 1.0. On the other hand, it is possi-ble that the individual does not take the drugand lives in a health state that is preferred overdaily nausea but for less time. Given these twosituations, it is entirely possible that QALYs willbe the same or even greater for those not takingthe drug. These quality values for the alternative

health states, also known as utilities, are eitherestimated as part of the study or gathered fromthe existing literature.

A commonly used approach to measuringbenefits in dollar terms (for CBA) is the willing-ness-to-pay (WTP) approach, which uses quan-titative approaches to estimate how much peopleare willing to pay for a good, service, or reduc-tion in health and well-being. Similar to obtain-ing utility values for health states, WTP valuesmay be assessed directly (asking), indirectly(observing behavior), or obtained from the liter-ature. Direct approaches use specific techniques,such as contingent valuation, to determine theindividual’s WTP. As a consequence of the timeand expense required to collect WTP values andindeed the difficulties associated with askingpeople to value life in dollar terms, fewer CBAshave been conducted than other evaluation typesfor healthcare services.

Several aspects of measuring costs and bene-fits should be considered in pharmacogenomicsstudies. As noted by Higashi and Veenstra [1],one important consideration in pharmacoge-nomics studies is that the cost of a genetic test-ing strategy includes much more than the costof the test itself. There are also potentialinduced costs such as long-term follow-up, test-ing of family members to assess heritable traits,and the costs of treatments pending the resultsof genetic tests. However, other potential uses ofthe genetic information obtained from testingcan provide long-term benefits. This is mostlikely to occur when the genetic variation affectsmore than one drug, as with the P450 metabolicenzymes, for example [3]. Thus, economic evalu-ations of pharmacogenomics will need to con-sider a wider range of possible effects andlonger-term outcomes than analyses of someother healthcare interventions.

c) Adjust costs and benefits/effects for time (discounting)It is generally agreed that both costs and bene-fits/effects should be discounted to net presentvalue to take into account costs and benefitsbeing realized at different times. Discounting thecosts of a pharmacogenomic strategy adjusts forthe perception that a dollar spent in the future isworth less than a dollar spent today. To helpunderstand discounting costs, Gold et al. (1996)likened discounting to the interest paid for aloan received today (e.g., if I borrow US$100today I might have to pay US$110 in thefuture), where a dollar paid in the future is worth

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less than a dollar paid today. Although histori-cally a 5% discount rate has been most com-monly used, the US Preventive ServicesTaskforce recommended 3% as the primary ratewith 5% used in sensitivity analyses [4].

d) Describe the conceptual model orevent pathwayThe conceptual model outlines an event pathwaystemming from the use of the intervention tohealth outcomes. It includes both the series ofhealth events and costs associated with thoseevents. For this step it is often useful to draw a‘picture’, such as a decision tree or flow diagram,that follows the patient from the relevant decisionpoint (e.g., take genetic test or not take genetictest) through to the relevant end point (e.g.,death). For example, Figure 1 shows a simple eventpathway that could be the core of a larger decisionanalysis model that would include the associatedprobabilities and outcomes. A genetic screeningprogram as a pretreatment strategy for prescribingdrug XYZ might require that the patient agree tothe test, adhere to any prescreening requirements(e.g., fasting), show-up to take the test, obtain atest result, and adhere to treatment and follow-up(Figure 1). Each of these steps is associated withcosts such as the cost of the test and the cost ofdrug treatment and monitoring. Each step is alsoassociated with effects such as changes in theprobability of disease progression if the treatmentis not followed or the adverse effects of a drug.

Such models can be analyzed using ‘decisionanalysis’, a systematic, quantitative approach forassessing the relative value of different decisionoptions. Decision analysis is used for economicevaluations as well as other types of complexdecisions where information is uncertain. Threecommonly used software programs include usingDecision Analysis by TreeAge (DATA 4.0™),Precision Tree® for MS Excel, and SMLTREE,which have been developed to simplify drawingand analyzing decision trees.

In the case of pharmacogenomic analyses, akey issue will be the effectiveness of the genetictest. Therefore, the researcher should considercharacterizing genetic test performance accord-ing to its analytic validity (i.e., sensitivity andspecificity), clinical validity (i.e., penetrance,positive and negative predictive values, andattributable risk), and clinical utility [26].

Example from systematic literature reviewMarchetti [21] developed a decision analyticmodel using DATA 4.0 to compare two

intervention strategies for patients with a firstepisode of DVT. Specifically, they used a Markovsimulation model to trace events over time, andcosts and effects were discounted at 3%.

Step 3: Calculate and present resultsa) Calculate and present primary resultsResults should generally include tables of thecosts and benefits/effects for each interventionconsidered. Most importantly, these shouldinclude the relevant incremental cost-effective-ness ratios (ICER) (e.g., cost per QALY gained).Incremental ratios compare each intervention tothe next most effective option after eliminatingoptions that are dominated (i.e., have higher costand lower effectiveness). It is also often useful toreport total costs and benefits/effectiveness inaddition to incremental costs and benefits/effec-tiveness, so that readers can understand how theoverall results were calculated. For a CEA ofpharmacogenomics comparing genotyping (1)versus not genotyping (2) prior to initiating drugtherapy, incremental costs are calculated by sub-tracting the costs of the genotyping (C1) fromthe costs of not genotyping (C2). Similarly,incremental effects are determined by subtract-ing the effects of genotyping (E1) from theeffects of not genotyping (E2). Thus, the incre-mental cost-effectiveness ratio represents “thedifference in costs between the two alternativesto the difference in effectiveness between thesame two alternatives (p 399)” [4]. Mathemati-cally the ICER is given as:

In cost-effectiveness studies, incrementalrather than average CEA ratios should usually bereported. Average cost-effectiveness ratios aretypically determined with respect to the ‘no-cost’/’no-effect’ alternative and are mathemati-cally given as:

Average cost-effectiveness ratio = C/E

Average cost-effectiveness ratios have thepotential to confuse the reader and incorrectlymisrepresent the cost-effectiveness of the alterna-tives. For example, suppose we are conducting aCEA to screen for gene X prior to initiating drugtherapy. The total cost of not screening isUS$5000 and the total effectiveness is 10QALYs. For the alternative, screening, the total

ICER C1 C2–E1 E2–--------------------=

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Figure 1. Simple eve

Physician ord

Physician not order

Geneticscreening

cost is US$8000 and the total effectiveness is 12QALYs. Therefore, the ICER for screening is(5000 - 8000)/(10 - 12) = US$1500. However, ifthe average cost-effectiveness for the screeningprogram were presented, the reader might mis-takenly think that the cost-effectiveness ofscreening compared to not screening was lowerat 8000/12 = US$667. This simple examplehighlights how the average ratio might be con-fused with the ICER; in reality, the no screeningalternative is often not associated with no-costsor no-effects.

b) Conduct sensitivity analyses to assess impact of changing the data inputs and modelThere are two key sources of uncertainty in eco-nomic analyses: (1) parameter uncertainty,which is uncertainty about the true numericalvalues of the parameters used as inputs, and (2)model uncertainty, which is both uncertaintyabout the model structure and uncertainty aboutthe combination of decisions made in the analy-sis. In a sensitivity analysis, critical componentsof the calculation should be varied and theresults recalculated to determine how sensitivethe results are to a specific input. For example,sensitivity analysis can show how the cost-effec-tiveness would differ if the genetic test were todecrease in price or if the prevalence of the muta-tion in the population was found to be greaterthan the best estimate. Sensitivity analysis can beconducted by varying the assumptions about onevariable and assessing the effect on the evaluationof the decision (one-way analysis) or by simulta-neously allowing assumptions about multiplevariables to vary and reanalyzing the decision(multi-way analysis). The value of multi-wayanalyses is that they take into account interaction

among the variables as well as the impact on thecost-effectiveness calculation.

Example from systematic literature reviewYounossi [22] provides a table with total costs,incremental cost, total effectiveness, incrementaleffectiveness, and the incremental cost-effective-ness ratio for each of the six chronic hepatitis Ctreatment strategies. This study also provides atable that compares the incremental cost-effec-tive ratio with other accepted interventions.One-way sensitivity analyses, as well as best-worst case analyses and model validation, werealso reported.

Step 4: Interpret results and place into contextThe final step of a CEA of a pharmacogenomicstrategy is to put the results into context for thereader and to clarify their meaning. This finalstep includes explaining the generalizability ofthe results from the study population to othergroups and interpreting the external validity ofthe results. Other important discussions mayinclude a review of results from other relevantstudies and the distributive implications (i.e.,who will gain and who will lose if a new strategyis implemented). As with other research studies,limitations that influence favorable cost-effec-tiveness should be explained to the reader. Forexample, suppose a clinical trial finds that a strat-egy to genotype both the proband and the first-degree relatives of the proband are cost-effectivecompared with no genotyping. While the posi-tive result of the CEA is encouraging, a limita-tion to the cost-effectiveness might be that in ageneral population, as opposed to a study popu-lation, first-degree relatives of the probandwould have to agree to genotyping. To generalize

nt pathway.

Patient compliant with treatment and followup

(Outcome)

Patient non-compliant with treatment and followup

(Outcome)

Genetic screening results used to guide

prescribing regime

Screening results not included in

prescribing strategy(Outcome)

Patient has test

Patient does nothave test

(Outcome)

Patient adheresto prescreeningrequirements

Patient does not adhere to prescreening

requirements(Outcome)

Patient agrees to test

Patient does not agree to test

(Outcome)

ers test

does test

(Outcome)

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Highlights

• It will be increasingly evaluation, especially

• Few economic evaluaand pharmacogenomor conduct these ana

• Key steps in conducti(1) Clearly define the (2) Assess costs, bene(3) Calculate and pres(4) Interpret results an

the result might therefore require knowing thepercentage of first-degree relatives for whom thiscould be expected.

Outlook and conclusionIn summary, we have provided an overview of thesteps generally required to conduct and interpretan economic evaluation, and we have providedspecific examples of published economic evalua-tions of pharmacogenomics for the reader to con-sider. This review is intended to provide the readerwho may be unfamiliar with economic evaluationwith a general guide that will assist with not onlyanalyses of the cost-effectiveness of pharmacoge-nomics but also with the ability to understand andevaluate the published literature. However, thereader is cautioned that the general guidelinesshown are neither exhaustive nor detailed withrespect to calculating costs or defining the out-comes of interest. Important components of con-ducting economic evaluations are not discussedhere. Readers embarking on their own economicevaluation of pharmacogenomics may find moredetailed discussion in recommended texts [4,5,14].

In the future, economic evaluation willbecome increasingly important to assess the costsand benefits of pharmacogenomics. However,there are currently few economic analyses ofpharmacogenomics, and studies cover a limitednumber of genetic mutations and diseases.Although the lack of cost-effectiveness evalua-tions of pharmacogenomics undoubtedly reflectsthe currently limited use of these technologies, itis important to systematically evaluate theirlikely costs and benefits before they are widelyimplemented. Previously [3,27], we identified keyfactors that are likely to determine the cost-effec-tiveness of pharmacogenomics, which need to beconfirmed with empirical analyses:

• Prevalence of the genetic mutation and thedisease in the population.

• Severity and cost of the disease or outcome thetest is designed to predict or diagnose.

• Strength of the association between thegenetic mutation and clinical outcomes (pene-trance).

• Availability of effective interventions that canbe implemented on the basis of genetic infor-mation that provide a reduction in the rele-vant event rate over standard care.

• Cost, turn-around time, and accuracy of thetest.

In conclusion, the expanded use of pharmacoge-nomics offers many potential clinical benefitsbut also many economic challenges. It will thusbe essential that systematic, evidence-based tech-nology assessments and economic evaluations beused to guide the incorporation of pharmacoge-nomics into clinical practice.

important to apply the methods of economic CEA and CBA, to pharmacogenomics.tions of pharmacogenomics have been conducted, ics researchers may be unfamiliar with how to review lyses.ng an economic evaluation are: research question and study frameworkfits/effects, and outcomesent resultsd place into context

BibliographyPapers of special note have been highlighted as either of interest (•) or of considerable interest (••) to readers.1. Higashi MK, Veenstra DL: Managed care in

the genomics era: assessing the cost-effectiveness of genetic tests. Am. J. Managed Care (In Press).

• Provides details on how to apply cost-effectiveness analysis to genetic testing.

2. Sullivan SD, Lyles A, Luce B, Grigar J: AMCP guidance for submission of clinical and economic evaluation data to support formulary listing in US health plans and pharmacy benefits management

organizations. J. Managed Care Pharmacy 7, 272-282 (2001).

3. Veenstra DL, Higashi MK, Phillips KA: Assessing the cost-effectiveness of pharmacogenomics. AAPS PharmSci. 2(3) (2000) E29. www.aapspharmsci.org.

•• Provides details on how to apply cost-effectiveness analysis to pharmacogenomics.

4. Gold M, Siegle J, Russell L, Weinstein MC: Cost-Effectiveness in Health and Medicine. Oxford University Press, NY, USA (1996).

• Widely used guide.5. Drummond MF, O'Brien BJ, Stoddart GL:

Methods for the Economic Evaluation of Health

Care Programmes (Second Edition). Oxford University Press, NY, USA (1997).

• Widely used guide.6. Swartz, K: The human genome and medical

care in the new century. Inquiry 37, 3-6 (2000).

7. Phillips KA, Veenstra DL, Sadee W: Implications of the genetics revolution for health services research: Pharmacogenomics and improvements in drug therapy. Health Services Res. 35(5), 1-12 (2000).

• Provides overview of economic issues relevant to pharmacogenomics.

8. Phillips KA, Veenstra DL, Oren E, Lee JK, Sadee W: Potential role of pharmacogenomics in reducing adverse drug

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reactions: a systematic review. JAMA 286(18), 2270-2279 (2001).

9. Robertson JA, Brody B, Buchanan A, Kahn J, McPherson E: Pharmacogenetic challenges for the health care system. Health Affairs 21(4), 155-167 (2002).

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