Common Errors and Controversies in Pharmacoeconomic Analyses

Common Errors and Controversies inPharmacoeconomic AnalysesSarah Byford and Stephen PalmerCentre for Health Economics, University of York, York, England

Summary The need to demonstrate the cost effectiveness of healthcare interventions hasled to a rapid increase in the use of economic tools within pharmaceutical eval-uations. Pharmacoeconomics is employed at many stages of the evaluation pro-cess, helping to predict which products are likely to be economically viable at anearly stage, and providing information to aid price and reimbursement negotia-tions as well as formulary and purchasing decisions in conjunction with phase IIIand IV clinical trials.

The ability of economic evaluations to accurately determine the best use ofsociety’s scarce resources, however, is strongly influenced by the existence ofareas of confusion, controversy and dispute which hinder the researcher at everystep. A good economic evaluation requires a number of ingredients including:(i) relevant, good quality clinical data, raising issues of trial design, sample sizeand perspective; (ii) relevant costs and outcomes, measured, valued and dis-counted credibly and accurately; (iii) appropriate methods of data analysis (sta-tistical, incremental and sensitivity); and, once the trial is over, (iv) presentationof the results in a way which maximises the generalisability of the results and,hence, the usefulness of the research. None of these areas are trouble-free butwith understanding and openness, mistakes can be minimised.

LEADING ARTICLE Pharmacoeconomics 1998 Jun; 13 (6): 659-6661170-7690/98/0008-0659/$04.00/0

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Pharmacoeconomics is big business; it can beemployed within clinical development programmesto predict which products are likely to be econo-mically viable, and, in conjunction with phase IIIand phase IV clinical trials, it can provide valuableinformation to aid price and reimbursement nego-tiations, and formulary and purchasing decisions.[1-5]

The fundamental aim of economic evaluationsshould always be the same: to determine the bestuse of society’s scarce resources, in terms of thebenefits gained from expenditures. The existenceof areas of confusion, controversy and dispute,however, make this aim a difficult one to achieve.Drawing on the extensive literature in this area andthe authors’ own experiences of the practical appli-cation of economic evaluation, this article dis-

cusses, from the health economists’ perspective,common obstacles to be overcome at each stage ofthe evaluation process, attempts to clarify areaswhere a consensus has been reached and highlightsaspects of evaluation where methodological confu-sions still exist and more research is required.

1. Study Design

Economic evaluations require good evidence ofclinical effectiveness, as well as good quality costdata; study design is, therefore, important.[5] Al-though pharmacoeconomic analysis encompassesa number of alternative techniques, including ob-servational data (e.g. before and after case-seriesand case-control studies) and decision analysis (e.g.Markov models, simulation models and decision

trees), it is widely accepted that a well conductedrandomised controlled trial (RCT) represents thegold standard in clinical and economic evalua-tion.[6] Indeed, this has been recognised in Austra-lian requirements for reimbursement.[7]

It is not uncommon, however, to find economicevaluations conducted alongside poor quality trialsor carried out retrospectively, thus increasing thelikelihood of collecting unreliable data.[8] Hille-man et al.,[9] for example, carried out a retrospec-tive, nonrandom comparison of 32 antihyperten-sive drugs for mild to moderate hypertension andconcluded that the mean costs for β-blockers werelower than for any other class of therapy. It is, how-ever, impossible to determine whether this conclu-sion is due to the interventions themselves or to,perhaps, patients with different characteristics be-ing prescribed different categories of drugs.

To assess efficacy, rather than effectiveness,many pharmaceutical evaluations employ explan-atory, placebo-controlled designs and limit analy-ses to treatment completers. For economic evalua-tions, however, a pragmatic, intention-to-treatdesign is preferred.[2,5,10-12] Pragmatism providesreal world information necessary to determinewhich treatments are more cost effective in routineclinical practice. Furthermore, treatment drop-outsmay involve significant costs in terms of the pro-vision of alternative interventions and the treat-ment of relapse or adverse events, which would beexcluded if an intention-to-treat design was not se-lected.[3]

The choice of comparators can have significantimplications on the design of a trial and ultimatelyon the cost effectiveness of the intervention underinvestigation. To assess the true cost effectivenessof an intervention, health economists agree that theappropriate comparator should be the next best al-ternative.[13] Identification of this alternative, how-ever, is not always obvious. At the very least, newinterventions should be compared with currentpractice, but where a number of possible alterna-tives exist, it may be necessary to include a rangeof comparators (e.g. the most widely used practiceas well as local practice) where these differ. Ad hoc

selection of comparators or the inappropriate useof placebo where alternatives exist will not enablethe true cost effectiveness of interventions to bedetermined.

Clearly, there is a general consensus that theRCT is the most appropriate study design for eval-uating interventions. When assessing both the ef-fectiveness and the efficiency of an intervention,however, it must be recognised that the use of pla-cebo-controlled trials should no longer be consi-dered sufficient and the selection of appropriatecomparator(s) should be given as much thought asthe choice of study design. The inclusion of healtheconomists early in the design stage of a trial cangreatly improve the usefulness of data collected foreconomic purposes and help to minimise the prob-lem of inconclusive results due to poor designs.[14]

2. Sample Size

An issue of critical importance to the healtheconomist is the adequacy of sample sizes for eco-nomic evaluations.[12] Gray et al.,[15] in a RCT ofcase management for people with mental disorders,demonstrated that the sample size was too small todetect relatively large cost differences.[15] To en-sure this does not become a common feature ofeconomic evaluations, economic issues must betaken into consideration alongside clinical out-comes when sample sizes are calculated. Drum-mond and O’Brien[16] argue that such calculationsshould be based on an estimate of a worthwhiledifference in costs and suggest that any differencegreater than the cost of changing to a new methodof treatment would be adequate.

In those cases where an intervention is unlikelyto be cost saving or cost neutral, however, less con-sensus exists regarding the most appropriate methodfor calculating sample size. Although calculationsshould be based on estimates of the predicted in-cremental cost per unit of outcome achieved, theexisting quality of evidence may be inadequate forthis purpose and calculations would have todemonstrate that this cost-effectiveness ratio is sig-nificantly lower (i.e. more favourable) than a pre-determined threshold value.[17] The selection of such

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a threshold value is fraught with controversy, sub-jectivity and ethical issues and further research isrequired before this debate is concluded.

3. Perspective

Economic evaluations of healthcare interven-tions commonly take the perspective of the healthservice, budget holder or pharmaceutical com-pany.[18] A review by Lee and Sanchez[19] of 65pharmaceutical studies published between 1985and 1990, revealed that 91% took the perspectiveof the healthcare provider. Economics, however, isconcerned with the impact of an action on the wel-fare of the whole of society, not just on the indivi-duals or organisations directly involved, and theexclusion of certain sectors may alter the conclu-sions of a study. Mynors-Wallis et al.,[20] for exam-ple, found no significant clinical differences whencomparing a community-based problem-solvingtreatment with ‘treatment as usual’ for emotionaldisorders; on the cost side, the problem-solvingtreatment was more expensive from the perspec-tive of the health service, but this was more thanoffset by savings in the cost of days off work. Awider perspective altered the relative cost effec-tiveness of the experimental intervention.

To certain interest groups, an evaluation carriedout from a societal perspective may seem unneces-sary. The inclusion of all relevant costs and bene-fits, however, will enable such groups to isolate theinformation relevant to their own perspective andwill also allow the effect of their actions on othersectors to be determined.[3] Indeed, a societal per-spective is recommended in Canadian guidelinesfor economic evaluation of pharmaceuticals, aswell as guidelines for pharmacoeconomic analysesto determine reimbursement eligibility in Ontario,England and Wales.[21-23] If a full societal evalua-tion is not possible, the study perspective shouldbe explicit and the exclusion of any items shouldbe explained and discussed in terms of their likelyinfluence on the final results.[10,24] Studies withnarrow perspectives may result in a suboptimal al-location of resources and a corresponding loss insocietal welfare.

4. Cost Measurement and Valuation

The first step towards the measurement and val-uation of costs is the collection of resource-usedata, raising the issue of which costs to include.Costs can be split into 2 main categories: (i) directcosts, which include treatment costs (the actualcost of treating an individual) and nontreatmentcosts (travel, informal care, etc.); and (ii) indirectcosts which refer to productivity losses resultingfrom premature death or disability.[25,26]

The need to include direct costs is not debated,yet studies which exclude relevant costs can stillbe found. For example, only drug costs were in-cluded in a study comparing ampicillin/sulbactamwith cefoxitin for prophylaxis in high-risk patientsundergoing abdominal surgery, and the study au-thors argued that all other costs could be assumedto be equal.[27] However, no evidence was pre-sented to support this assumption. Relevant directcosts should always be included in an economicevaluation unless there is empirical evidence tosupport their exclusion.

The inclusion of indirect costs is more contro-versial, due mainly to criticisms of the valuationmethods employed. Indirect costs are often valuedon the basis of gross earnings, ignoring the fact thatthe existence of unemployment allows workerswho leave the labour force to be replaced at littlecost. Hence, attention has recently turned to thefriction-cost method of calculation which, morerealistically, attempts to account for the level ofscarcity in the labour market. A useful guide to thismethod is provided by Koopmanschap and Rut-ten.[25] Various pharmacoeconomic guidelines rec-ommend the inclusion of indirect costs includingthose from Canada, England and Wales.[21-23] How-ever, Luce and Elixhauser,[28] in common with Aus-tralian guidelines,[7] suggest excluding indirectcosts unless inclusion is likely to have a large im-pact on the results and, thus, on policy.

Although from a health economist’s perspective,the inclusion of indirect costs is required, currentmethods of valuation are still debated and until aconsensus has been reached, researchers must selecta preferred method while alluding to the problems

Errors and Controversies in Pharmacoeconomic Analyses 661


of that form of valuation. By reporting direct andindirect costs separately, the likely importance ofindirect costs in the area under consideration canbe assessed.

Once resource-use data have been collected,unit costs must be calculated. One mistake commonto many studies is the use of charges to approxi-mate costs.[5,29] The health economists’ definitionof cost (opportunity cost) does not necessarily re-late to the price paid for a service, but to the bene-fits (or opportunities) lost by not directing thosescarce resources to their best alternative use.[28] Toimprove the quality of pharmacoeconomic data, se-rious attempts must be made to calculate opportu-nity costs; researchers must not continue to rely oneasily available but, possibly, inaccurate informa-tion.

5. Outcome Measurementand Valuation

The end-points selected for measurement inclinical trials may not always provide the best datafor an economic analysis, as they commonly mea-sure changes in biomedical indicators using dis-ease-specific scales rather than capturing the fullrange of effects an intervention may have on a per-son’s health.[2,5,30] Many disease-specific scalesexist and different researchers will often use differ-ent scales, making inter-trial comparisons impossi-ble. Furthermore, combining costs with multidi-mensional outcomes which have been measured ona number of different disease-specific scales is dif-ficult, particularly if patients improve on somescales but not on others.[31]

The alternative is to use generic scales which aredesigned to measure all aspects of the quality of aperson’s life and, therefore, can be more widelyapplied than disease-specific scales. Some genericscales value individual items of health separatelyand cannot easily be collapsed into a single mea-sure of outcome, such as the Medical OutcomesStudy 36-Item Short Form (SF-36) health surveyprofile.[32] To generate a single index value ofhealth-related quality of life (HR-QOL), a utilityscale is needed, such as the quality-adjusted life-

year (QALY). Despite a general consensus amonghealth economists regarding the need to employmeasures of HR-QOL, there is as yet no universallyaccepted scale. Until such agreement is reached, itmust be recognised that current available scales,such as the EuroQOL instrument[33] and the Rosserand Kind Index,[34] are subject to criticism regard-ing methodological problems and valuation diffi-culties,[35,36] and are unlikely to generate the sameQALY score for a given health state.

HR-QOL indices will not necessarily be sensi-tive enough to detect small or specific changes inhealth status, but used alongside more detailedgeneric profiles or disease-specific scales, assess-ments of their accuracy and sensitivity can bemade. Lawrence et al.,[37] in a study comparing lap-aroscopic with open repair of inguinal hernia, providea good example of a study which has employed anumber of alternative forms of effectiveness scales,including the EuroQOL, to assess quality of life andlinear analogue pain scores to provide a more sensi-tive examination of a relevant biomedical end-point.[37]

Whatever scales are chosen, attention should bepaid to their validity and reliability, and any limi-tations made explicit. Gandhi and Kong,[38] in areview of 76 clinical trials of antihypertensivedrugs which included a measure of quality of life,found only 20% had provided any information onthe reliability of the scale and only a similar pro-portion provided evidence of the scale’s validity.

6. Discounting

Although health economists agree on the needto discount costs that occur in the future to presentvalues, there is no firm consensus on the most ap-propriate rate to employ (although a rate of 5% ismost frequently cited[39]) nor is there agreement onthe need to discount benefits.

The importance of failing to discount costs canbe illustrated with reference to an elementary errorin a study on prenatal screening for cystic fibro-sis.[40] Although the study authors reported thatscreening represented good value for money basedon a crude cost-benefit analysis, the study failed to

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discount averted treatment costs. Had they dis-counted at a rate of 5 to 10%, using the same studymethodology, their conclusion that cystic fibrosisscreening was worthwhile would have been re-versed.[41,42]

In an analysis of hepatitis B vaccination, Manga-tani et al.[43] clearly demonstrate the need for thepotential impact of discounting benefits to be ex-plored. Discounting years of life gained in the fu-ture at 0%, vaccination in infancy was the mostcost-effective policy, compared with no vaccina-tion, followed by vaccination in pre-adolescence,with selective vaccination the least cost effective.When years of life gained were discounted at 6%per year, however, pre-adolescent vaccination be-came the most cost-effective strategy.

Although recent pharmacoeconomic guidelinesrecommend that both costs and outcomes shouldbe discounted at a rate of both 3 and 5% to allowcomparability with existing studies,[13] given thelack of a general consensus, whichever rate is se-lected, an explanation for the choice should begiven and sensitivity analysis should always be un-dertaken to explore the effect of a range of rates onthe results of a study (e.g. 0 to 10%).

7. Incremental Analysis

Incremental analysis, the comparison of alter-natives in terms of the additional benefits obtainedfor the additional costs,[44] becomes a crucial issuein the reporting of economic evaluation resultswhen an intervention is found to be both morecostly and more effective than the comparator.Failure to calculate incremental cost-effectivenessratios, however, is still common. In a recent reviewof the economics of benign prostatic hyperplasiatreatment, only 1 of 6 articles identified as beingsuitable for incremental analysis had actually per-formed the analysis.[45]

Martens and Guibert[46] illustrate the impor-tance of incremental analysis in a cost-effectivenessanalysis of HMG-CoA reductase inhibitors (i.e.statins) in the primary prevention of coronary heartdisease.[46] While the average cost per year of lifesaved for the drugs included was similar [fluvas-

tatin 40mg at $US38 200; simvastatin 10mg at$US48 300; lovastatin 20mg at $US53 000; and pra-vastatin 20mg at $US56 200 (1993 values)], rela-tive to fluvastatin 40mg, the incremental cost-effec-tiveness ratios were approximately 2.3 to 6 timesgreater than the average ratios [simvastatin 10mgat $US88 200; lovastatin 20mg at $US198 100 andpravastatin 20mg at $US330 300 (1993 values)].The use of average cost-effectiveness ratios can,thus, clearly mislead decision-makers and it is es-sential that studies present an incremental analysiswhenever appropriate.

8. Sensitivity Analysis

The importance of employing sensitivity analy-sis to test the robustness of a study’s conclusionshas been well documented[47,48] and is reflected inpharmaceutical guidelines which recommend boththe incorporation of sensitivity analysis and thequantitative reporting of these analyses in phar-macoeconomic evaluations.[7,21,22,49] However, re-cent reviews of the literature have shown that al-though many pharmacoeconomic studies citelimitations in the underlying assumptions, the useof explicit techniques to determine the likely im-pact of variation in these assumptions is less com-mon.[50,51] Agro et al.[50] reported that only 59% ofstudies reviewed had actually conducted sensitivi-ty analyses. Furthermore, where sensitivity analy-sis has been performed, a large proportion of thesehave been judged to be limited in scope; Briggs andSculpher[51] reported that only 39% of studies re-viewed had given an adequate account of uncer-tainty.

To improve the use, techniques and presentationof the results of sensitivity analyses, existingguidelines provide valuable information on thepreferred methods of sensitivity analysis and theselection of parameters.[7,21,22,49] At a minimum, ithas been recommended that researchers performunivariate sensitivity analyses on all parameters inan economic evaluation and conduct multivariatesensitivity analysis on important parameters whichmay have a major impact on the results.[13]



9. Statistical Analysis

The trend towards conducting prospective eco-nomic evaluations alongside clinical trials in-creases the opportunity for measuring the wholedistribution of costs rather than simply producinga point estimate, allowing statistical tests of eco-nomic hypotheses to be performed[17,52] and uncer-tainty in stochastic data to be quantified using con-fidence intervals.[53]

However, errors arising primarily from the pe-culiarities often associated with resource-use andcost data are common. In particular, many studiesfail to consider the nature of the distribution of theresource-use and cost data which will often beskewed due to a relatively small proportion of pa-tients consuming a relatively large proportion oftotal costs. Such non-normally distributed datarender t-tests inappropriate and alternative meth-ods of analysis must be found. Creed et al.,[54] com-paring day and inpatient psychiatric treatment, em-ployed the nonparametric Mann-Whitney U test.Alternatively, Rutten-van Molken et al.[55] suggesta log-transformation of the data to reduce the im-pact of extreme values and create similar size vari-ances to enable parametric testing. This techniquewas used by Gray et al.[15] in a study of case man-agement for mental disorders when the cost datawere found to have a large standard deviation andto be highly positively skewed.

The calculation of confidence intervals aroundcost-effectiveness ratios is considered particularlyimportant because the economic importance of achange in cost can only be considered in combina-tion with the clinical importance of changes ineffect.[16] Although currently there is no generalconsensus on the most appropriate method of con-ducting such statistical analysis,[17] Polsky et al.[56]

argue that the routine reporting of confidence in-tervals would enable decision-makers to makemore informed judgements about the value-for-money of an intervention.

10. Generalisability

Issues related to the generalisability of researchfindings affect pharmacoeconomic analysts whowant to ensure that their study results can be ap-plied as widely as possible and decision-makerswho must interpret the results of studies conductedin settings different to their own.[57] Althoughmany issues relating to generalisability can be ad-dressed by close adherence to the methodologicalprinciples necessary to conduct a ‘good’ economicevaluation,[26,58,59] there is much that analysts cando when reporting results to aid both the compara-bility and generalisability of studies.[58]

Common mistakes still being made include: (i)failure to provide an adequate description of theintervention and comparator under investigation;(ii) reporting total costs without reporting the physi-cal quantities of resources used and unit costs sep-arately;[60] and (iii) failure to adequately addressany study limitations.[13] Increased transparencyregarding the methods, assumptions and data em-ployed in pharmacoeconomic analyses can greatlyassist decision-makers in interpreting the results ofindividual studies in a more generalised context.[57]

11. Conclusion

Economic evaluation is not fool proof; compli-cations, confusions and disputes exist which renderthe ‘perfect study’ difficult, if not impossible, toachieve.

From the health economist’s perspective, thisreview has discussed a number of common obsta-cles to be overcome at each stage of an economicevaluation. Although discussed in isolation, it mustbe recognised that there is likely to be a significantlevel of interdependence between these areas. Forexample, if the perspective adopted and costs in-cluded are inappropriate, then problems relating tothe generalisability of the findings are likely to bemagnified. By improving the quality of data col-lected and the methodological approach adopted ineach individual area, however, the potential impactof interdependence will be reduced.

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In a number of the areas which commonly causeconcern, a general consensus among health econo-mists has been reached and mistakes or omissionsare no longer excusable. For example, it is gener-ally agreed that: (i) a well designed trial, preferablya RCT, and a societal perspective are required; (ii)all relevant costs should be included and dis-counted to present value where necessary; (iii) ameasure of HR-QOL should be employed along-side clinically relevant outcome scales; and (iv)statistical, sensitivity and incremental analysisshould be used where appropriate.

Undoubtedly, areas remain where debate con-tinues and further research is required before a con-sensus view can be achieved (e.g. appropriatemethods of valuing indirect costs, appropriatemethods of incorporating economic issues in thecalculation of sample size, development of a uni-versally accepted measure of HR-QOL, selectionof an appropriate discount rate, whether or not todiscount benefits and development of statisticaltechniques appropriate to stochastic cost data). De-spite this, there is no excuse for researchers to ex-clude these areas from analysis; methods or tech-niques considered to be most appropriate should beselected and justified and potential problems madeexplicit and discussed in terms of their effect onthe results. Similarly, all assumptions made shouldbe explained, supported by evidence where avail-able and tested using sensitivity analysis. Only inthis way will the quality, usefulness and reputationof pharmacoeconomic analyses improve.

Acknowledgements

The authors are grateful to Linda Davies for commentson an earlier draft and to the anonymous referees for theirsuggestions for improvement.

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Correspondence and reprints: Sarah Byford, Centre forHealth Economics, University of York, Heslington, YorkYO1 5DD, England.E-mail: [email protected]

666 Byford & Palmer


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Common Errors and Controversies in Pharmacoeconomic Analyses