Pharmacoeconomics: State of the Art in 1997

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February 26, 1997 14:17 Annual Reviews AR028-21 AR028-21

Annu. Rev. Public Health. 1997. 18:529–48Copyright c© 1997 by Annual Reviews Inc. All rights reserved

PHARMACOECONOMICS:State of the Art in 1997

Kevin A. Schulman and Benjamin P. LinasClinical Economics Research Unit, Division of General Internal Medicine,Georgetown University Medical Center, Washington, D.C. 20007;e-mail, [email protected]

KEY WORDS: cost-effectiveness, prospective data collection, pharmaceutical products,medications, economic analysis

ABSTRACT

Economic evaluation of pharmaceutical products, or pharmacoeconomics, is arapidly growing area of research. Pharmacoeconomic evaluation is important inhelping clinicians and managers make choices about new pharmaceutical pro-ducts and in helping patients obtain access to new medications. Over the lastfew years, the scientific rigor of this field has increased greatly. At the sametime, new types of analysis, based on prospective data collection, have beendeveloped. This article reviews the basic concept of pharmacoeconomics, thetypes of data available for economic evaluation, and the “state of the art” inpharmacoeconomics as reported in the medical literature.

INTRODUCTION

Economic pressures on health care systems throughout the world have helpedpropel the development of methods for evaluating the costs and outcomes ofmedical care. Pharmacoeconomics, the study of the costs and benefits of med-ical therapies and technologies, combines economics, epidemiology, decisionanalysis, and biostatistics in a comprehensive evaluation of treatments. Thoughpharmacoeconomics is a relatively new field, pharmacoeconomic data have be-come increasingly important for reimbursement decisions made by nationalhealth programs and private health care organizations (6, 16, 22, 33). In ad-dition, the process of economic evaluation has quickly become more sophis-ticated, as users of the information become increasingly critical reviewers ofpharmacoeconomic studies (3, 15, 17, 39). As a result of the growing demand

5290163-7525/97/0510-0529$08.00

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530 SCHULMAN & LINAS

for high-quality economic studies, the 1990s have seen a trend toward devel-opment of prospective economic data in an effort to improve the validity ofpharmacoeconomic assessments. Recognizing that prospective studies requirea great deal of resources, economic researchers have begun advocating an inte-gration of economic evaluation concepts throughout the clinical developmentprocess of new pharmaceutical therapies (37).

As pharmacoeconomics becomes a more regular part of pharmaceutical de-velopment and marketing, clinicians will be required to assess the validity andimportance of the growing number of economic claims for new products. Thisarticle reviews the basic concepts of pharmacoeconomics, recent methodolog-ical advances in the field, and the current status of the pharmacoeconomicliterature.

ECONOMIC CONCEPTS

Economic evaluation is the assessment of both the costs and the benefits ofmedical care. The costs of care may fall into four categories: 1. direct medicalcosts, which are the costs of medical services (for example physician, hospital,or pharmaceutical services); 2. direct nonmedical costs, which are the costsincurred in receiving medical care (such as transportation to and from a spe-cialty treatment center); 3. indirect costs, which are the costs of morbidity andmortality due to illness; and 4. intangible costs, which are the costs of pain andsuffering related to illness. An economic assessment may include any or all ofthese different types of costs.

Costs are calculated from different points of view or perspectives. Costs ofmedical care can be calculated from the viewpoint of the patient, caregiver,payer, or society. The calculation of societal costs represents the total cost ofa transaction. The calculation of costs from other perspectives includes onlythe costs relevant to the party of interest. For example, consider a patient witha $1000 hospitalization and an insurance policy that requires a 20% copayment.From the patient’s perspective, the cost of the hospitalization is $200, from thepayer’s prospective, the cost is $800, and from a societal prospective, the costis $1000. An economic evaluation can measure costs from a single perspectiveor from multiple perspectives. All evaluations must, however, assume some per-spective in calculating costs, as “cost” can only be identified from a given pointof view. In reading economic studies, it is therefore important to remain sensi-tive to the types of costs that may be excluded by the reported study perspective.

In addition to closely reviewing cost data, readers of pharmacoeconomic stud-ies must critically appraise reports of treatment benefits or outcomes. Whileassessing treatment benefits is one of the most critical elements of a pharma-coeconomic investigation, measuring the clinical benefits of medical care is one

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PHARMACOECONOMICS 531

of the most difficult methodological challenges of pharmacoeconomics. Clin-ical benefits can include clinical outcomes, such as changes in blood pressureor survival, or changes in patients’ quality of life or preferences for their healthstates (43). The measurement of outcomes like patient preferences within alongitudinal clinical trial setting is an entirely new undertaking.

TYPES OF ANALYSES

There are four major types of analyses performed in pharmacoeconomic as-sessments of new therapies: cost-benefit, cost-effectiveness, cost-utility, andcost-identification. Each analysis assesses the cost of medical care but differsin its measurement of the benefits of care.

Cost-benefit analysiscompares the cost of a medical intervention to its ben-efit. Both costs and benefits are measured in the same (usually monetary) units(e.g. dollars). The analysis can be used either to compare the total cost andtotal benefits of two treatments, or to compare the additional costs and benefitsassociated with using one therapy over another. When researchers wish to com-pare the costs and benefits associated with using one therapy over another, theyassess the incremental cost and incremental benefit of the therapy of interest.The incremental cost of a new therapy is the cost of the therapy as compared tothe cost of conventional medical care. Likewise, incremental benefit comparesthe benefit of a new therapy to that of conventional care. Incremental analysis isgenerally preferred to comparisons of total costs and benefits because it allowsanalysts to focus on the differences between two treatment modalities.

One of the potential difficulties of cost-benefit analysis is that, because itrequires researchers to express costs and outcomes in identical monetary units,money values must be associated with the different health states observed ina clinical trial. Outcomes (treatment benefits) may be difficult to measure inmonetary terms. Further, many ethical questions arise in developing monetaryvalues for treatment outcomes. Though economists believe that cost-benefitanalyses are the most appropriate for determining new health care investments,such analyses are rarely pursued owing to the measurement and ethical issuesinvolved.

Cost-effectiveness analysis, the most common pharmacoeconomic approach,compares costs of an intervention in monetary terms with effectiveness of the in-tervention measured in clinical units. For example, one might measure clinicaloutcomes in terms of number of lives saved or number of toxicities prevented.Alternatively, health outcomes may be reported in terms of change in an inter-mediate clinical outcome, such as percent change in blood cholesterol level orpercent lowering of blood pressure. Results of cost-effectiveness analyses aregenerally reported as a ratio of costs to clinical benefits (for example, dollars

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per year of life saved). Again, researchers can compare total costs and effec-tiveness or the incremental cost-effectiveness of a new therapy as compared toconventional care.

Cost-utility analysismeasures costs of an intervention in monetary units andoutcomes in terms of the value patients gain from medical therapy. Ratherthan expressing outcomes in terms of objective clinical measures, cost-utilityanalysis is concerned with patients’ subjective valuations of the benefits theyreceive from therapy. The analysis requires patients or society to assign a value,known to economists as autility value, to overall treatment outcomes (43). Autility value is a measure of patients’ preferences for their health states or for theoutcome of an intervention. Researchers typically use utility values to createa quality-adjusted outcome measure. For example, on a scale from 0 to 1,where 0 is the worst imaginable health state and 1 represents perfect health, aresearcher could determine that patient X, who has severe allergies, feels thathis health state is equal to .9. The outcome measure, quality-adjusted life years(QALYs), is calculated using both survival and patient preference information(44). If the patient lived 20 years with a condition that causes him to feel thathe has a health of .9, he would have 18 QALYs.

Investigators are currently exploring the use of patient-preference assess-ment in clinical trials (41). One of the challenges of cost-utility analysis isdeveloping a valuation of a patient’s health state. Generally, techniques thatelicit preferences under conditions of uncertainty are considered “true” utilitymeasures, whereas techniques that do not elicit preferences under uncertaintyyield “value” measures.

The most common preference-assessment instruments in the clinical trialsetting are the EuroQolc© (5), the Quality of Well Being (QWB) scale (20),and the Mark Health Utilities Index (Mark HUI-III) (7, 8, 43). Both the QWBand Mark HUI-III assess functional status across specific domains and thencombine these measures into a single preference or utility score based on apopulation preference assessment for each component of the different healthstates described by the instrument. The EuroQolc© instrument asks for patientevaluation of their current health state scored on a 0 to 100scale, where 0 is theworst imaginable health state and 100 is the best imaginable health state.

In addition to using developed survey instruments, researchers can rely onface-to-face interview techniques in developing utility values for heath states.The interview methods use risk or uncertainty to elicit patient preferences.The standard gamble (43, 44) and the time trade-off (27, 31) are the two mostcommonly used interview methods. The standard gamble offers the patient ahypothetical choice between a definite health state of defined impairment or agamble that offers a probability of living the rest of life in perfect health anda probability of immediate death. Patient preference for a given health state is

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Figure 1 Illustration of possible study results.

calculated from the point of indifference between the two options. Time trade-off offers a choice between a defined state of health impairment or a shorterduration of life in perfect health. Researchers alter the length of time that thepatient would live in perfect health to determine how much life the patient wouldbe willing to trade in exchange for a higher health state. The indifference pointis again used to calculate the patient’s utility value for the health state.

Interpretation of cost-benefit, cost-effectiveness, and cost-utility studies isbased on the relationship between the costs and benefits of the strategies beingassessed. There are four potential results from an economic study (Figure 1)(40). An improvement in clinical outcome at a reduced cost of medical services(+/+, upper left-hand corner) is considered adominant strategyand shouldbe adopted. A strategy that results in increased costs while yielding reducedclinical outcomes (−/−, bottom right-hand corner) is considered adominatedstrategyand should be rejected. Mixed results (the shaded blocks,+/− and−/+), are ambiguous, and their interpretation depends on the magnitude of thechanges in cost and clinical outcomes as compared to the base-case strategy.In such ambiguous cases, cost-effectiveness analysis can be used to comparethe costs of the medical services provided relative to their clinical outcomes inmaking treatment or policy decisions.

Cost-identification analysissimply enumerates the costs involved in medicalcare, ignoring the resulting outcomes. Using cost-identification analysis, re-searchers can determine the costs of alternative means for providing a medicalservice. Results are typically expressed in terms of cost per unit of serviceprovided. For example, a cost-identification study might measure the cost of acourse of a specific antibiotic regimen or the cost of a surgical treatment pro-gram. It would not, however, calculate the clinical outcomes (that would be

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cost-effectiveness analysis), or the value of the outcomes in units of currency(that would be cost-benefit analysis). Because they compare treatments basedon costs alone, cost-identification studies are appropriate only when treatmentoutcomes or benefits of the therapies being evaluated are known to be equivalent.

STUDY DESIGNS

There are several study designs for economic evaluations of clinical therapies:prospective or retrospective analysis of clinical trials, retrospective analysis ofspecific patient populations, analysis of administrative data bases that describethe experience of a specific patient population defined by the type of healthcare payer, and decision-analytic models that predict the effectiveness of ther-apies using existing clinical data. Each design can be used in isolation or as acomponent of a larger clinical and economic assessment of a new screening ortreatment strategy. The strengths and limitations of each of these study designsare discussed in the following paragraphs.

Prospective economic studies of clinical trials are based on random assign-ment of patient populations to treatment groups. The design protects againstbiases in patient assignment and ensures appropriate comparison groups for theassessment of new therapeutic strategies. The prospective economic evalua-tions included in these studies are designed specifically to measure the costsand benefits of new pharmaceutical therapies.

Prospective economic evaluation can be included in all phases of the clin-ical development process—phases I through IV. Ideally, economic models oftherapy are developed in phase I, pilot testing of data-collection instruments iscompleted in phase II, pivotal assessment of therapies is completed in phase III,and assessments of different comparative agents or “real-world” effectivenessstudies of therapies are completed in phase IV. Economic evaluation requirescareful examination of the clinical protocol to identify biases that might affectthe economic study. In addition, researchers must review the study populationto insure the generalizability of the clinical trial. Assessment of trial endpointsfor economic studies may require different follow-up procedures than those re-quired for clinical endpoints, such as the addition of an economic data-collectionstrategy. Often, in prospective economic studies, the resources required to carefor patients are recorded in the clinical case report forms, whereas the costsof the resources are collected outside the clinical trial. Data sources for theresource costing efforts include tracking patients through administrative databases, hospital billing records, physician or faculty practice plans, outpatientlaboratory billing records, and billing records for home care, hospice, and infu-sion services. Assessment of a patient’s out-of-pocket medical costs and qualityof life usually requires direct patient assessment (12, 17, 31, 35, 43, 44).

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Though economic analysis is based on consideration of lifetime cost andeffects of treatment or screening strategies, the observed period in most clinicaltrials does not extend over a patient’s lifetime. Thus, prospective economicanalysis may evaluate study results for two time periods: the period observedwithin the study, and a projection of the impact of the therapy over a patient’slifetime. Projection models estimate effects of therapy beyond the period ofthe study based on the impact of therapy during the trial period. Economicinvestigators have proposed the use of both conservative, or one-time-effect,and optimistic, or continuous-effect, assessments for these projections (38).

Retrospective economic analyses have been performed on clinical trials thatdid not originally include an economic component. These studies are limitedby the lack of some patient-specific information (e.g. out-of-pocket costs) andthe potential for incomplete data recorded in medical records. Quality-of-lifeinformation for patients who receive new therapeutic strategies may not beretrievable retrospectively. Even with comprehensive data for specific sites,such as medical records from a study center, economic data are frequentlymissing or incomplete. Patients may receive nonprotocol therapies, consultnonstudy physicians, and be rehospitalized outside of the clinical trial center.These data may not be recorded on patients’ charts and would therefore beunavailable to a retrospective economic analysis.

Administrative data bases have been widely used to assess costs of medicalcare (28, 48) and patterns of patient care across populations defined geograph-ically (10, 23, 24, 29,) or demographically (4, 13, 21). However, analysesbased on administrative data alone may be severely limited. Though adminis-trative data bases usually include comprehensive information about proceduresreceived by patients, they lack detail about the patient’s clinical condition. Insuch data sets, ICD-9 and CPT-4 coding systems are used to describe diagnosesand treatments. These coding systems may not provide sufficient informationon diagnosis or comorbidities to allow effective identification of specific typesof patients, treatments, or treatment complications. Serious design flaws inthese studies may also limit their generalizability. Because patients are notrandomly assigned to treatment, there may be differences in stage of illness orcomorbidities across treatments. Attempts to use these data to make compar-isons between treatment groups may therefore be inappropriate.

Economic evaluation of new pharmaceutical therapies may also include thedevelopment of decision-analytic models of treatment received by patients.These models allow assessment of complicated clinical issues that would requireyears to test through prospective studies. Frequently, these studies combineclinical data from several sources, including existing medical literature andmedical chart abstraction. By assessing the sensitivity of the model to variationsin model parameters, decision-analytic studies can identify the most important

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factors driving both costs and outcomes of clinical care [e.g. one could assessthe potential effects of pap smear frequency on the diagnosis of cervical cancerfor a cohort of patients (2)]. Decision-analytic models have been very effectivein assessing treatment (14, 36). Often, however, the most important factors inan economic model are those that are unknown or poorly assessed in the clinicalliterature. Clearly, decision-analytic models cannot address such shortcomings.Increasingly, decision-analytic models are being used to streamline clinical trialdesign by helping investigators identify the most critical elements to assesswithin the study (42).

EMERGING RESEARCH CONCEPTS

The past year has been an exciting one in pharmacoeconomic research. Phar-macoeconomics began with the development of decision-analytic models thatassessed new pharmaceutical therapies (47). In the 1980s, as more powerfulhardware and software tools enabled more complicated analytic efforts, themodels became more sophisticated (14, 36). Pharmacoeconomics was trans-formed in the early 1990s by the development of prospective data collectionstrategies for pharmacoeconomic studies. These efforts included prospectiveeconomic studies designed as secondary endpoints within phase III clinical tri-als, and new types of phase IV trials that compare new pharmaceutical productsto competitors in a “real-world” setting. While prospective pharmacoeconomicstudies are only a small proportion of all economic studies reported in the litera-ture, the number of reported prospective studies is expected to increase over thenext several years as investigations initiated in the early 1990s are completedfollowing several years of data collection and analysis.

Investigators have reported several issues that need to be addressed in de-signing future prospective studies (1, 21, 39). Most of these articles address thespecific data elements that should be included in a prospective economic study,both from a theoretical and applied perspective. More fundamentally, investi-gators have posited conditions where economic analysis should be consideredan important endpoint for a clinical trial (1), and they have proposed means ofexamining the external validity of economic analyses (30). Because economicevaluation requires a significant investment of time and resources, prospectiveevaluation of therapies is most important for new and innovative therapies andmay be considered less critical for compounds that are therapeutically equiva-lent to previously existing agents.

Analysis of economic data collected in trials is subject to some of the samecriticisms and challenges as analysis of traditional clinical trial data. For exam-ple, missing data may be distributed on a random or nonrandom basis (patientpreference information may be missing because a patient became too ill to

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complete the instrument, a nonrandom loss of data). Further, economic datamight be left-censored if economic data collection efforts are not implementedat the time of trial initiation, or may be right-censored if data collection doesnot continue through a patient’s lifetime (the latter case is true for most clinicaltrial data). Further methodological research is needed to determine the bestmechanism for imputing missing data. Most likely, such research will developnaturally over the next several years, as investigators have the opportunity toanalyze newly completed prospective studies.

Economic analysis has historically included a sensitivity analysis to deter-mine the stability of reported results to changes in model parameters. Withprospective data collection, however, researchers have begun to move to a con-fidence interval framework to estimate uncertainty in prospective economicstudies (32).

Given the importance of economic evaluation in the reimbursement process,the design of clinical trials themselves has come under increased scrutiny. Whileclinical trials were initially designed to provide safety and efficacy data, theyare now also called upon to provide economic information to policy-makersdeciding reimbursement rates for new therapies. In light of the increasingvalue placed on economic data, researchers may want to consider an economicframework to develop an appropriate design for pivotal clinical trials (35).

RECENT REPORTS

In a review of the pharmacoeconomic literature published in 1995–1996, wefound evidence of increasing analytic complexity in decision analyses, as well asseveral reported prospective economic studies. In addition, a new methodologicframework for economic evaluation was proposed by an expert panel convenedby the US Public Health Service (12a). The impact of this work should be seenin the next several years.

Below we review some of these recently published economic studies cho-sen because they illustrate many of the principles discussed above. Whereappropriate, we point out potential weaknesses in the reports in an effort toalert the reader to some of the common pitfalls and challenges of economicresearch. Articles are grouped below by type of study.

DECISION-ANALYTIC MODELS

Pharmacoeconomic Analysis of Ondansetron VersusMetoclopramide for Cisplatin-Induced Nausea and VomitingZbrozek et al (50) report the results of a model to assess the costs and benefitsof ondansetron and metoclopramide in relieving nausea in patients receiving

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high-dose cisplatin therapy. Data were collected from a meta-analysis of clinicaltrials reported in the medical literature. Clinical outcomes measured includedthe number of emesis episodes occurring within 24 hours of treatment andthe number of extrapyramidal reactions occurring after metoclopramide wasadministered. Quality-of-life data were based on an extrapolation from themedical literature.

The authors developed a decision tree for their analysis considering only di-rect costs from the perspective of the hospital. In the cost analysis, ondansetronis reported to cost $95.50 per 40-kg patient and metoclopramide to cost $50.95per 40-kg patient. Emesis rates were estimated at 2.03 and 2.69 per patientfor ondansetron and metoclopramide, respectively. The cost-utility analysisrevealed that the incremental cost of ondansetron was $168,391 ($407,667) perQALY gained in 40-kg (70-kg) patients when compared to metoclopramide.The authors conclude that ondansetron offers a small clinical benefit at a veryhigh price.

This study was developed to address the lack of comparative data on alterna-tive therapies for cisplatin-induced nausea and vomiting. Despite its interestingconclusion, it requires further validation, especially with respect to patients’perceptions of their treatment benefits. Estimating utility values for short-termchanges in health status is extremely challenging, and this analysis may besensitive to the utility estimates used in the paper. Utility estimates are notaddressed in the sensitivity analysis.

Doxycycline Compared with Azithromycin for TreatingWomen with Genital Chlamydia Trachomatis Infections:An Incremental Cost-Effectiveness AnalysisMagid et al (25) report the results of a decision analysis comparing two treat-ments for uncomplicated cervical chlamydial infection: doxycycline 100 mgorally twice daily for 7 days, and azithromycin 1 g orally administered as a sin-gle dose. Clinical data were based on expert opinion using a modified Delphitechnique. Cost data were based on allowed charges for a large health planand average wholesale prices for the medications (the authors did not includea dispensing fee in their analysis).

Under baseline assumptions, azithromycin was associated with an increase ineffectiveness secondary to an increase in compliance. Thus, despite the higheracquisition cost of azithromycin, the treatment had a lower overall cost thandoxycycline. The authors performed a series of sensitivity analyses, includingunivariate and multivariate analyses. In univariate sensitivity analysis, whendoxycycline effectiveness was greater than .93, azithromycin prevented moresequellae of the infection, but at a higher cost. In a multivariate sensitivity anal-ysis designed to be a worst-case scenario for azithromycin, the incremental cost

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of azithromycin therapy was $521 per additional major complication avoidedwhen compared to doxycycline. When the cost of azithromycin dropped be-low $9.80, however, azithromycin was the dominant therapy, with both lowercosts and better outcomes. The authors conclude that azithromycin is the morecost-effective therapy for uncomplicated cervical chlamydial infection.

The authors designed this study to address the lack of comparative data forthe two antibiotic regimens. The decision tree model included an assessment ofpharmaceutical acquisition price, as well as the costs of complications from un-treated infections. However, this study relied upon expert opinion for treatmentefficacy data. Thus, the results of this report require further validation.

Alternative Management Strategies for Patientswith Suspected Peptic Ulcer DiseaseFendrick et al (9) report the results of a decision analysis assessing the cost-effectiveness of several therapies for treating peptic ulcer andHelicobacterpylori in persons with symptoms suggesting peptic ulcer disease. The authorsdeveloped this analysis as a simulation model based on a hypothetical cohort of1000 patients. The interventions evaluated include: 1. immediate endoscopyfor peptic ulcer and biopsy forH. pylori, 2. immediate endoscopy withoutbiopsy, 3. serologic test forH. pylori, 4. empiric treatment with antisecretorytherapy, and 5. empiric treatment with antisecretory therapy and antibioticagents to eradicateH. pylori. Results were expressed in terms of cost per ulcercured and cost per patient treated. Clinical and probability data were taken fromthe literature. Cost data were taken from the national average reimbursementallowed for Medicare patients by the Health Care Financing Administration, apayer’s perspective.

The cost per ulcer cured for each treatment was 1. $8045 for immediateendoscopy for peptic ulcer and biopsy forH. pylori, 2. $6984 for immediateendoscopy without biopsy, 3. $4541 for serologic test forH. pylori, 4. $4835 forempiric treatment with antisecretory therapy, and 5. $4155 for empiric treatmentwith antisecretory therapy and antibiotic agents to eradicateH. pylori. The costper patient treated was 1. $1584, 2. $1375, 3. $894, 4. $952, 5. $818.

Multivariate sensitivity analysis showed that the cost-effectiveness of non-invasive strategies (strategies 3, 4, and 5) relative to invasive strategies (1 and2) was sensitive to only two variables: 1. cost of endoscopy and 2. the prob-ability of recurrent symptoms in patients not initially treated with endoscopyand in whom peptic ulcer was not the underlying cause of symptoms. The au-thors conclude that, because invasive diagnostic testing is expensive, treatmentstrategies that economize on invasive testing may be preferred.

This study was developed due to the lack of clinical data reported in the med-ical literature, and it reports some interesting hypotheses about the management

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of patients with suspected ulcer disease. Unfortunately, further validation maynot be possible owing to the large number of patients that would be required tobe enrolled in a prospective study of this question.

A Cost Analysis of Alternative Treatments for Duodenal UlcerImperiale et al (18) also report the results of a decision analysis to comparethe costs of several strategies for treating duodenal ulcer, though the analysisassessed patients with endoscopically confirmed ulcers. Strategies assessedincluded: 1. H2-receptor antagonist therapy for 8 weeks, 2. antibiotic therapyfor H. pylori infection plus H2-receptor antagonist therapy for 8 weeks, and3. urease test-based treatment. For symptomatic recurrences, secondary treat-ment strategies included empiric treatment with the same or another regimenand treatment based on repeat endoscopy-guided urease test or biopsy, with anassumption of subsequent cure. Analysis included development of a decisiontree, with probability estimates based on the medical literature and expert opin-ion. Cost data were developed from several sources, including Medicare fees,and were developed from the perspective of a group practice-model HMO.

Regardless of secondary treatment, initial therapy with antibiotics forH. py-lori plus an H2-receptor antagonist resulted in the lowest average cost per curewhen the prevalence ofH. pylori infection exceeded 66% to 76%. Initial treat-ment with H2-receptor antagonist treatment alone resulted in the highest costsper cure. Results were robust in one-way sensitivity analyses but were sensitiveto some combinations of prevalence, efficacy, and costs. The authors concludethat, no matter what course of secondary treatment is selected, initial treat-ment with antibiotic therapy forH. pylori infection plus H2-receptor antagonisttherapy for 8 weeks is the most cost-effective therapy.

Like previous examples, this study was developed due to the lack of clinicaldata reported in the medical literature. While it reports some interesting hy-potheses about the management of patients with suspected ulcer disease, thestudy requires further validation. Again, however, validation efforts may notbe possible due to the large number of patients that would be required for aprospective investigation. The authors provide an excellent illustration of theirdecision tree and report several graphs based on the results of their sensitivityanalyses.

Cost-Effectiveness of Warfarin and Aspirin for Prophylaxisof Stroke in Patients with Nonvalvular Atrial FibrillationGage et al (11) used decision analysis to compare the cost-effectiveness ofwarfarin and aspirin in patients with nonvalvular atrial fibrillation (NVAF) withor without additional stroke risk factors. Clinical data were developed fromthe medical literature. Cost data were also based on reports from the medical

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literature and were developed from the societal perspective. Long-term survivalwas estimated using a Markov model, and quality-of-life data were gathered byconducting a time trade-off interview with 74 patients suffering from NVAF.Results are expressed in terms of quality-adjusted survival and marginal cost-effectiveness of warfarin as compared to aspirin and no therapy.

For patients with NVAF and additional risk factors for stroke, warfarin ther-apy led to increased quality-adjusted survival and cost savings due to the lowerincidence of stroke (49). For patients with NVAF and one additional risk fac-tor for stroke, warfarin cost $8000 per QALY saved. In 65-year-old patientswith NVAF alone, warfarin cost $370,000 per QALY saved as compared withaspirin, and in 75-year-old patients with NVAF alone, warfarin cost $110,000per QALY saved. In patients not prescribed warfarin, aspirin provided bothbetter quality-adjusted survival and lower costs than no therapy, regardless ofthe number of risk factors.

Sensitivity analysis demonstrated that the results were most sensitive to therate of stroke, followed by the effectiveness of aspirin therapy. Different esti-mates of the rate of major hemorrhage while patients were on warfarin therapyalso impacted cost-effectiveness. The authors conclude that warfarin is cost-effective in patients with NVAF and more than one risk factor for stroke butnot in 65- to 69-year-olds with only one risk factor. In all patients for whomwarfarin is not the preferred treatment, aspirin therapy is preferred to no therapy.

Again, this study was developed to address a question where clinical datawere lacking in the medical literature. The study is based on a Markov modelallowing different treatment outcomes to be modeled over time. Again, themethods and results are well reported.

Cost-Effectiveness of Interferon-α2b Treatmentfor Hepatitis B e Antigen-Positive Chronic Hepatitis BWong et al (49) report the results of a meta-analysis and Markov simulationmodel to determine the cost-effectiveness of interferon-α2b treatment for hep-atitis B e antigen-positive chronic hepatitis B. Effectiveness results were basedon data from 552 patients reported in the medical literature. Resources requiredto care for patients were estimated based on expert opinion, and costs weredeveloped using variable hospital and physician costs from the societal per-spective. Quality-of-life data were again developed based on expert opinion.

Results show that interferon-α2b increases the likelihood of testing negativefor HBeAg and of testing negative for hepatitis B surface antigen. For a 35-year-old individual who has hepatitis B and is HbeAg positive, interferon-α2bincreases life expectancy and quality-adjusted life expectancy at a cost saving,even when discounting future expenditures. In sensitivity analysis, the cost-utility ratio of interferon-α2b never exceeded $12,000 per QALY gained. The

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authors conclude that interferon-α2b should prolong life and lower costs forpatients with chronic hepatitis B.

This study was developed to assess the efficacy of this treatment strategy inthe absence of long-term clinical trial data. The authors based their effectivenessdata on a meta-analysis of reports in the medical literature, and they extrap-olate from intermediate clinical outcomes (effects of treatment on serologicviral markers) to long-term survival. Thus, this analysis provides an importantassessment of the potential benefits of treatment. Again, further validation ofthe model is required. Specifically, investigators will need to assess whetherchanges in serologic viral markers lead to a change in sequellae of hepatitis Binfections. Unfortunately, such an analysis may take a great deal of time, asthese sequellae are late complications of the disease.

PROSPECTIVE EVALUATIONS

Cost-Effectiveness of Captopril Therapyafter Myocardial InfarctionTsevat et al (45) assess the cost-effectiveness of captopril therapy for survivorsof myocardial infarction (MI) using a decision-analysis model. Four-year sur-vival data were taken directly from the Survival and Ventricular Enlargement(SAVE) trial. Cost and health state data were developed from a subset ofpatients treated at one study site. Cost data were developed from a societalperspective. The SAVE trial showed that captopril therapy improves survivalin MI patients with an ejection fraction less than or equal to 40%. Long-termsurvival was estimated using a standard Markov model. The study assessedthe cost-effectiveness of captopril therapy in 50- to 80-year-old survivors ofMI with an ejection fraction less than or equal to 40%. In one set of analyses,the authors assumed that captopril had extended benefits beyond the four-yearstudy period (persistent benefit analysis). In another set, the authors assumedthat captopril therapy had continued costs but no benefits beyond four years(limited benefit analysis).

In the limited benefit analysis, the incremental cost of captopril was $3600per QALY gained for 80-year-old patients and $60,800 per QALY gained in 50-year-olds. In the persistent benefit analysis, incremental costs ($/QALY gained)ranged from $3700 to $10,400 depending on age. In a “worst-case” analysis,in which captopril was assumed to have no benefits beyond four years and wasnot associated with any savings in hospital costs, other cardiac medications, orphysician follow-up visits, yet was associated with a 1% decrement in quality oflife, cost-effectiveness ratios ranged from $8700 to $29,200 per QALY gainedin patients aged 60 to 80 years old, but equaled $217,600 per QALY gained in50-year-old patients.

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The authors conducted the study because of a lack of long-term data aboutthe cost-effectiveness of captopril therapy. Data inputs for the model weredeveloped from a subset of patients enrolled in the SAVE study, although theauthors do not assess whether their sample was representative of the trial popu-lation as a whole. The projection model provided an important example of theuse of economic models to project clinical trial data into time periods beyondthose observed within the clinical trial. The use of two projection models, thelimited benefit and persistent benefit model, was similar to a method reportedpreviously (38).

Cost-Effectiveness of Thrombolytic Therapywith Tissue Plasminogen Activator as Comparedwith Streptokinase for Acute Myocardial InfarctionMark et al (26) use data from the GUSTO trial of tPA and streptokinase (SK)to assess the cost-effectiveness of tPA in the treatment of acute MI. One-yearclinical data were based on the results of the 41,021-patient GUSTO trial. Lifeexpectancy after one year was projected using data from the Duke Cardiovas-cular Disease Database. Resource consumption data were based on the initialhospitalization for 23,105 US patients and from one-year follow-up data on asubsample of 2600 US patients who had structured telephone interviews overa 12-month period. Costs for these resources were developed from a hospitalcost-accounting system and from Medicare reimbursement rates and were de-veloped from the societal perspective. In their primary analysis, the authorsassume that tPA results in no extra medical costs following the one-year end-point of the clinical trial and that the survival benefit of tPA was still presentfollowing one year.

At the end of one year, tPA had both higher costs and a higher survival ratethan SK. Discounting at 5%, tPA had a marginal cost of $32,678 per QALYgained as compared to SK. tPA was most cost-effective in older patients andless so in younger patients.

Sensitivity analyses show that the cost-effectiveness ratios are sensitive toassumptions about life expectancy and medical costs for the tPA group after thefirst year. Results were robust to assumptions about the prevalence of strokedue to tPA.

The authors conclude that the cost-effectiveness of tPA falls within the rangeof other medical therapies currently accepted in the medical community.

This is an excellent example of an economic analysis conducted alongsidea clinical trial, in this case a very large clinical trial. The investigators feltthey were overpowered for their secondary economic study, and developed theinnovative sampling strategy reported in the manuscript. However, the authorsdid not report whether the subgroups in their analysis (all US patients and the2600 follow-up patients) were representative of the study population as a whole.

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This may be important if the clinical benefit in the United States is differentfrom that observed outside the country. Obviously, there is much less concernabout the validity of this analysis than about the validity of the decision analyticmodels reviewed in the previous section of this paper.

Results of the Economic Evaluation of the FIRST Study:A Multinational Prospective Economic EvaluationThe study reports results from a phase III clinical trial of epoprostenol, a potentvasodilator that was proposed for the treatment of patients with congestive heartfailure (34). The study was stopped on the recommendation of the study safetyand data monitoring committee in July 1993 because of increased mortality inthe epoprostenol-treated patients. The report, however, demonstrates the feasi-bility of collecting prospective economic data alongside phase III clinical trials.

Resource consumption and quality-of-life data were collected as an inte-gral component of the clinical trial. Costs were developed from a single UShospital and from a societal perspective. Outcomes were measured using theEuroQolc© instrument and the Nottingham Health Profile (NHP). Results ofthe survival analysis were reported in terms of quality-adjusted life-months(QALM). The NHP was used as a predictor in regression analyses predictingQALMs, as were variables describing patient characteristics and study site.In addition, the authors conducted two-tailed t-tests comparing average costs,days in the hospital, months of life, and QALMs for study and control patients.Because data were collected prospectively, the authors were able to construct95% confidence intervals around reported results.

Results show that epoprostenol patients had costs that were $5022 greaterthan the costs for patients receiving usual care alone excluding the costs ofepoprostenol. Epoprostenol patients had no significant change in survival andhad nonsignificantly lower quality-adjusted survival than control patients.

The authors conclude that though the study was clinically disappointing,the economic arm of the study demonstrates the feasibility of conducting aneconomic analysis alongside a phase III clinical trial.

This study is another excellent example of a prospective economic assessmentof a clinical trial, in this case a multinational phase III clinical trial. The analysiswas very complex, and included the development of a confidence interval aroundthe study results using a bootstrap method. The study was limited in that thecost data were from only a single institution.

Costs and Effects of Long-Term Oral Anticoagulant Treatmentafter Myocardial InfarctionThe study by van Bergen et al (46) reports the results of the economic analysisof the ASPECT trial, a randomized, double-blind, placebo-controlled trial of

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oral anticoagulant therapy. Patients were followed for hospital resource use fora mean period of 37 months. Resource use was obtained through chart reviewfor all study patients admitted to a hospital during the study period and fromaverage rates of clinical monitoring by thrombosis centers in the Netherlands.Cost data were based on the guidelines of the Dutch Ministry of Health. The costof hospital stay [all costs in Dutch Guilders (Dfl)] was estimated at Dfl 773,irrespective of the intensiveness of care. Costs of medical procedures wereestimated based on the weighted average of published unit costs for Dutchhospitals. Sensitivity analysis was used to test the robustness of the analysis tochanges in several measured parameters.

The clinical trial showed that patients who received anticoagulant therapyhad fewer recurrent MIs and fewer cerebrovascular events, but more incidentsof major bleeding than those who received placebo. Results show that averagecost per placebo patient for follow-up care was Dfl 10,784 ($6254). Averagecost per anticoagulant patient was Dfl 9878 ($5729). The authors conclude that,in the long run, the costs of anticoagulant treatment are outweighed by the costsof prevented clinical events.

This paper is an excellent example of an economic evaluation of a prospec-tive clinical trial. The results are clearly presented, and a sensitivity analysis isperformed. The authors did not have primary data on outpatient medical care,but their estimates of the costs of monitoring anticoagulant therapy seem ap-propriate. However, costs of other outpatient medical services are not includedin their analysis. It is also not clear if the study costs represent the societalperspective in the Netherlands (although it appears that this is the case).

SUMMARY

The desire for economic evaluation of clinical trials has led to the develop-ment of a new discipline, pharmacoeconomics. Methodologic work in the fieldhas developed rapidly in the last decade. As reviewed in this paper, pharma-coeconomic data based either on sophisticated decision-analytic models, or,increasingly, on prospective data collection within clinical trials, are improvingour understanding of the costs and benefits of clinical therapies. These data areincreasingly important to clinicians and health policy decision-makers as theystruggle to determine the most appropriate uses of limited health resources.The methods of economic assessment of new pharmaceutical products are in-creasing in sophistication at a rapid pace. In large part, this development isstimulated by the analytic challenges posed by assessing pharmacoeconomicdata collected prospectively in clinical trials. It will be interesting to watch howthese new analytic tools affect the assessment of clinical data in clinical trialsin the next few years.

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ACKNOWLEDGMENTS

We thank Damon Seils for his help in the final production of this paper. Wealso thank John M Eisenberg, Henry Glick, and Harris Koffer for their helpin formulating the conceptual underpinnings of this article. Sections of thispaper were influenced by previous work of the authors (40). K Schulman issupported by a faculty development award in pharmacoeconomics sponsoredby the PhARMA Foundation.

Visit the Annual Reviews home pageathttp://www.annurev.org.

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Pharmacoeconomics: State of the Art in 1997