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Hill SR, Mitchell AS, Henry DA. Problems With the Interpretation of Pharmacoeconomic Analyses: A Review of Submissions to the Australian Pharmaceutical Benefits Scheme. JAMA. 2000;283(16):2116–2121. doi:10.1001/jama.283.16.2116
Author Affiliations: Discipline of Clinical Pharmacology, School of Population Health Sciences, Faculty of Medicine and Health Sciences, The University of Newcastle, New South Wales (Drs Hill and Henry); and Pharmaceutical Evaluation Section, Pharmaceutical Benefits Branch, Department of Health and Aged Care, Woden, Australian Capital Territory (Mr Mitchell), Australia.
Context Pharmacoeconomic analyses are being used increasingly as the basis for
reimbursement of the costs of new drugs. Reports of these analyses are often
published in peer-reviewed journals. However, the analyses are complex and
difficult to evaluate.
Objective To describe the nature of problems encountered in the evaluation and
interpretation of pharmacoeconomic analyses used as a basis for reimbursement
Data Sources All major submissions to the Department of Health and Aged Care (DHAC)
by the pharmaceutical industry for funding made under the Australian Pharmaceutical
Benefits Scheme. Specifically, the DHAC's database of submissions that were
received between January 1994 and December 1997 were reviewed.
Study Selection Of a total of 326 submissions, 218 had serious problems of interpretation
and were included in the analysis. The nature of the serious problems reviewed
were classified as estimates of comparative clinical efficacy, comparator
issues, modeling issues, and calculation errors.
Data Extraction All submissions in the DHAC's database were reviewed and data were extracted
if both the DHAC evaluators and technical subcommittee considered problems
to have a significant bearing on the decisions of the parent committee.
Data Synthesis Of a total of 326 submissions, 218 (67%) had significant problems and
31 had more than 1 problem. Of the 249 problems identified, 154 (62%) related
to uncertainty in the estimates of comparative clinical efficacy, and 71 (28.5%)
related to modeling issues, which included clinical assumptions or cost estimates,
used in the construction of the economic models. There were 15 instances of
disagreement over the choice of comparator, and serious calculation errors
were found on 9 occasions. Overall, 159 problems (64%) were considered to
Conclusions Significant problems were identified in these pharmacoeconomic analyses.
The intensive evaluation process used in the Australian Pharmaceutical Benefits
Scheme allowed for identification and correction of pharmacoecomomic analysis
problems, but the resources that are required may be beyond the capacity of
many organizations, including peer-reviewed journals.
Capped health budgets and rising drug costs have led to a high level
of interest in the use of economic analysis for decisions about the purchase
and subsidization of new pharmaceutical products.1
Economic analysis has been adopted by a number of agencies, including national
governments and managed health care organizations.2
The aim of pharmacoeconomic analysis is to relate any improved health outcomes
with new drugs (compared with established treatments) to the net costs associated
with their use. The results tend to be used to make decisions about the availability,
purchase, and pricing of new drugs.
Recognizing the importance of this emerging discipline and the potential
for bias, a number of commentators have published guidelines designed to improve
the quality of pharmacoeconomic analyses.3-5
Particular concerns have been the accuracy of the clinical assumptions that
underpin economic models and the nature of relationships between commercial
sponsors and consultants who are responsible for carrying out the analyses.5-8
In Australia, a satisfactory pharmacoeconomic analysis is required for
listing new drugs on the federal government's Schedule of Pharmaceutical Benefits.9 Since the introduction of this requirement in 1993,
more than 300 submissions containing pharmacoeconomic analyses have been submitted
by the pharmaceutical industry and subjected to detailed appraisal. This article
describes the problems that were encountered during the evaluation and interpretation
of the submissions evaluated between 1994 and 1997. The intention of this
article is to discuss the implications of these findings for decision makers
and journal editors who have to deal routinely with pharmacoeconomic analyses.
The Australian Pharmaceutical Benefits Scheme and the process for evaluating
drugs have been described previously.10-12
Subsidization of prescription drugs for use in the community (excluding public
hospitals) is a Commonwealth (federal) government function. The Pharmaceutical
Benefits Scheme is a comprehensive, publicly funded insurance program that
reimburses pharmacists for the costs of a selected range of prescription drugs.
There is a system of co-payments, and drugs are placed in different categories
of access, based on evidence of their comparative effectiveness and cost-effectiveness
in defined patient groups. Decisions to place new drugs in the Pharmaceutical
Benefits Scheme are made by the federal health minister on the advice of a
statutory committee, the Pharmaceutical Benefits Advisory Committee (PBAC).
This is made up of family medicine practitioners, specialist physicians, clinical
pharmacologists, pharmacists, and a consumer representative. The PBAC receives
advice from a technical economics subcommittee comprising individuals with
expertise in the fields of health economics, decision analysis, clinical epidemiology,
Pharmaceutical companies make submissions in support of listing new
drugs (or altered indications for existing drugs) to the Department of Health
and Aged Care (DHAC). The companies follow guidelines issued by the DHAC,9 which lay out a detailed format for presenting the
necessary data. Analysts, who may be employees of the company or external
consultants, prepare the submissions.
Each submission is subjected to detailed evaluation by staff at the
DHAC and their consultants. Typically, such an evaluation takes up to 2 person-weeks
and involves checking the literature search used in compiling the submission,
verification of trial results, validation of key assumptions in models, and
confirmation of resource costs according to a manual of Australian costs.
Evaluators rerun literature searches, check original clinical sources, and
frequently run computer models that are provided by the sponsors. Members
of the technical subcommittee of PBAC review the sponsor's submission and
the departmental evaluation. The subcommittee produces a summary document
outlining key issues and the implications these have for recommendations made
by the parent committee. The PBAC considers the sponsor's submission, evaluation,
report from the subcommittee, sponsor's response to the evaluation, and views
of its members when making its final recommendation to the federal health
The DHAC maintains a database of all applications evaluated since 1994
(Table 1). We reviewed submissions
received between January 1994 and December 1997.
We regarded problems in the submissions as "significant" if both the
evaluators and technical subcommittee considered that the problem could have
a significant bearing on the decisions of the parent committee. For instance,
a positive recommendation for listing a new drug might be negated if the supporting
data did not provide persuasive evidence of superior efficacy (compared with
the established treatment), or where correction of a spreadsheet error made
a cost-effectiveness ratio unattractive. To categorize the problems we used
an article by O'Brien13 describing some of
the difficulties encountered during the conduct of pharmacoeconomic analyses.
O'Brien listed 7 serious issues, and we modified these to produce the categorization
given in Table 2.
The database of submissions and the committee reports were scrutinized
by 2 of authors (S.R.H. and D.A.H.). If a problem was noted in the committee
report, it was categorized
according to Table 2.
Differences of opinion between the investigators were resolved by consensus.
Where appropriate, confidence intervals (CIs) for the proportions were
calculated using the normal approximation.
In compiling this review we were bound by the secrecy provisions of
the Australian National Health Act (1953, § 134A). The secrecy provisions
of the act prevent the publication of detailed information that might reveal
an individual drug, so we are able to identify drugs only by their clinical
In the period 1994-1997, the PBAC reviewed 326 major applications. Of
these, 182 were applications for new listings on the Pharmaceutical Benefits
Scheme, and 51 were applications for major changes in indications, conditions
of use, or prices applying to drugs that were already listed. The remainder
were resubmissions (where a submission has been rejected previously) or a
review of the basis of its pricing negotiations requested by manufacturers.
Of 326 submissions, 279 (86% [95% CI, 82%-89%]) contained economic analyses
based on the results of randomized trials. A total of 238 submissions (73%
[95% CI, 68%-78%]) contained head-to-head trial comparisons of the new agent
and the chosen comparator. In 41 cases the comparisons between the new drug
and comparator were made by considering the results of 2 sets of a trials
with a common reference, usually placebo. Twenty-six submissions were based
on quasi-experimental designs, and 21 were based on uncontrolled data. In
all, 64 submissions (19.6%; 95% CI, 15%-24%) based their estimates of comparative
clinical effectiveness on the results of meta-analyses. Of these, 17 were
published meta-analyses; during the last year of this survey (1997), 2 of
these were drawn from the Cochrane Library.
Of 326 submissions, 218 (67% [95% CI, 62%-72%]) were considered to have
presented serious problems of interpretation. Thirty-one submissions had more
than 1 problem. The proportion of submissions with problems did not change
over time: 69.7% in 1994, 69.2% in 1995, 63.1% in 1996, and 65.9% in 1997.
A total of 249 serious problems were identified; the numbers in each category
are summarized in Table 2. Overall,
159 problems (64%; 95% CI, 58%-70%) were considered to have been avoidable
during the planning and conduct of the pharmacoeconomic analysis.
Problems in this category reflected uncertainty about the magnitude
of any clinical benefit of the new drug compared with existing agents. The
weight given to this reflects the priority the PBAC gives to establishing
the comparative clinical performance of new drugs before making a judgement
regarding their economic performance.
Availability of Trials. In a number of submissions, including those relating to drugs for Parkinson
disease, breast cancer, and ovarian cancer, no randomized trial was available,
and data from uncontrolled studies were used to estimate comparative cost-effectiveness.
The magnitude of benefit of each drug had to be inferred from uncontrolled
comparisons of series of patients receiving the drug of interest or the comparator.
In some cases, data from a case series involving the new agent were compared
with the outcomes from the single arm of a randomized controlled trial involving
In a further group that included drugs for the treatment of chemotherapy-induced
vomiting, glaucoma, asthma, intermittent claudication, and bacterial vaginosis,
difficulties arose because of incomplete presentation of potentially relevant
trials. Literature searches performed by the evaluators identified randomized
trials that were relevant to the clinical comparison in the economic analyses,
but had not been mentioned by the sponsor. The data from these trials modified,
and in some cases contradicted, the claims made by the sponsor.
Poor-Quality Trials. In 31 submissions that included treatments for cancer, insomnia, osteoporosis,
and osteoarthritis, the key clinical trials had serious methodological flaws.
For example, a submission for a new drug claimed that it had a lower rate
of adverse effects, but this was based on an open-label trial with an inadequate
sample size and unblinded assessment of subjective outcomes.
Analysis and Interpretation of Trial Results. The statistical analyses carried out in submissions were often complex
and involved a range of meta-analytic techniques, subgroup analyses, or reanalyses
of clinical trial data. Problems included inappropriate subgroup analysis,
inappropriate adjustment of event rates, and problems with statistical pooling
Approximately 20% of submissions used meta-analyses of the results of
several randomized trials to estimate the comparative clinical benefit of
the new drug. In 3 instances, involving drugs for diabetes, osteoporosis,
and human immunodeficiency virus (HIV) infection, difficulties were encountered
when interpreting pooled data from crossover trials.
In a group of submissions including drugs to treat psychosis and eradicate Helicobacter pylori, problems arose while linking the evidence
from the randomized trials to the proposed indication. This was because the
proposed use was for "second-line" treatment (at a higher proposed price),
whereas the trials had been conducted in a "first-line" setting. It was not
clear that patients with more severe or "resistant" disease would respond
in the same way as those who had been included in the randomized trials.
A number of submissions, including drugs for cancer, asthma, prevention
of thromboembolic disease, and treatment of respiratory tract infections,
were based on claims that the new products were superior to the comparators.
The trials were considered to be of a satisfactory standard, but evaluators
disagreed with the sponsors' claims. There were 2 common explanations—the
trials were insufficiently powered to show differences between 2 active treatments,
or the demonstrated differences in outcomes were considered to be clinically
Use of Surrogate Outcomes. Evaluation was sometimes hampered by reliance on surrogate outcomes.
Examples included submissions for drugs for dementia that used measures of
cognitive function rather than measures of social coping; drugs for Paget
disease of bone, where biochemical tests were used rather than symptoms or
measures of disability; and urinary flow rates and lower urinary tract symptom
scores in men undergoing treatment for benign prostatic hypertrophy, rather
than reduction in surgery or prevention of progression to renal failure.
Determining Therapeutic and Dose Equivalence. The most frequent problem involved determining whether the available
data for a new product supported the manufacturer's claim that the product
was therapeutically equivalent to a comparator. There were also difficulties
in determining therapeutically equivalent doses. The latter is important during
price setting by cost minimization analysis.
In many cases, including drugs for hypercholesterolemia, Paget disease,
and hormone replacement therapy, the uncertainty was because trials were too
small to exclude clinically significant differences or were conducted over
too short a time. In assessing dose equivalence, difficulties arose because
of the design of the comparative trials, for example, a comparison of fixed
doses of 1 product with variable doses of the comparator.
The Australian guidelines request sponsors to use as comparator the
"treatment most likely to be replaced" by the new therapy. There were 15 examples,
involving drugs for Parkinson disease, epilepsy, infectious diseases, and
osteoporosis, in which there were disagreements regarding the correct choice
of comparator. In some cases, no comparator was nominated, and no comparative
data were provided. In other cases, the comparator that was nominated was
the least prescribed and most expensive alternative.
When there are no randomized trials that directly compare the new drug
and the comparator, the Australian guidelines advise companies to base their
submission on the results of an indirect comparison. This involves 2 sets
of trials, with placebo or another active drug as a common reference. Assessing
equivalence or superiority in this situation has proved difficult because
of confounding at study level and variation in the baseline severity of disease
in the control groups of the respective trials. Examples of this type of problem
include anticonvulsant drugs and drugs for Parkinson disease and HIV infection.
Technical Aspects of Model. These problems involved examples such as discounting costs but not benefits
(discounting is the technique used in economic evaluation
to calculate the present values of costs and consequences of an intervention,
given that the effects of the intervention may occur in the future rather
than the present14), failing to relate costs
and outcomes appropriately, and uncertainties arising from extrapolating benefits
seen in a short-term trial over the lifetime of the patient. More recent submissions
have used models based on willingness-to-pay studies and time-trade-off analyses.
Problems identified in this group in particular have included inappropriate
questionnaire design and inadequate sample size.
Unsubstantiated Assumptions. Two main issues emerged in this group. In submissions that involved
preventive treatments in osteoporosis, hypertension, and hypercholesterolemia
the models provided estimates of benefit that were biologically implausible
and unsupported by controlled clinical data. The second problem was the derivation
of utility estimates from insignificant or uncertain clinical data. This was
evident in several submissions for cancer therapies and also for drugs used
in the treatment of symptoms of relatively benign conditions, such as minor
Estimation and Incorporation of Costs. In some instances, cost offsets were unduly optimistic, without supporting
data, or benefits and associated cost savings were overestimated because of
inappropriate or truncated analyses. Lack of transparency in the calculation
of costs and outcomes was a significant problem, despite the fact that the
submissions usually contained copies of spreadsheets or models.
These problems included failure to calculate an incremental ratio correctly,
unbalanced numbers of patients assigned to alternative treatments in a model,
and simple spreadsheet errors that resulted in a product being erroneously
portrayed as dominant.
Establishing a formal link between measures of costs and outcomes of
drug therapy is an appealing approach to drug purchasing decisions. This comes
closer to the notion of a true "market" than alternatives, such as leaving
manufacturers to obtain the highest prices from poorly informed purchasers
or government control of profits as practiced in the United Kingdom.15 The variations seen in the prices of very similar
drugs in many countries16 reflect a degree
of market failure. This arises when purchasers or insurers use imperfect tools
to assess the community's needs, the extent to which a range of drugs meets
these needs, and at what price. There are strong arguments for linking purchasing
and pricing decisions to the therapeutic performance of drugs; this is the
central argument for the use of pharmacoeconomic analysis. Our experience
indicates that even when operating under a prescriptive regime, with clear
guidelines underpinned by strong legislation, problems frequently arose during
the conduct and interpretation of pharmacoeconomic analyses.
The bulk of problems concerned the interpretation of comparative clinical
data. Drug development programs mainly are designed to meet the needs of drug
regulatory authorities. Standards for evaluating efficacy, toxicity, and manufacturing
quality are well established.17,18
These represent an attempt to balance the need for adequate evidence on efficacy,
safety, and quality with the desire of the community for rapid access to important
new drugs and the commercial interest of manufacturers in obtaining licensing
approval as soon as possible. Pharmacoeconomic data are used in an environment
that is competitive and market driven. The desire of manufacturers is to show
new products in the most favorable light and establish an advantage over their
competitors. Unfortunately, the highest-quality data are sometimes suboptimal
for performing analyses of comparative efficacy and costs.19
The Australian guidelines provide a rationale for choosing the most appropriate
comparator when conducting pharmacoeconomic analyses. Typically, it is the
drug most likely to be replaced by a new product. This depends on local factors,
and high-quality comparative trials of the new drug and this comparator may
not be available. The unsuitability of the available data and the desire of
companies to get their drugs to market sometimes lead to claims of superiority
over competitors that are not supported after close examination of the clinical
Our impression is that analysts involved in the design and conduct of
pharmacoeconomic analyses can have difficulty substantiating claims made by
companies regarding the comparative clinical performance of their products.
We found that estimates of clinical performance were sometimes based on uncontrolled
studies, including case series, comparisons of single arms from different
trials, or inadequately conducted comparative trials. Companies often relied
on indirect comparisons, made through 2 sets of trials with a common comparator
(eg, placebo). The Australian guidelines encourage the use of such evidence
when direct comparative trials are unavailable; however, estimates of comparative
performance are highly likely to be confounded by factors that are unequally
distributed between the different study populations. Flawed estimates of comparative
clinical performance were sometimes used to justify a claim for higher prices.
Some of these claims violated well-established rules of epidemiological and
We believe that our experiences revealed no basic intention to deceive.
The occasional failure to present relevant trials became less common after
the guidelines made full disclosure a requirement. Where there were errors,
for instance simple numerical miscalculations, or mistakes in transcribing
probabilities into a decision tree, they were readily detected by examination
of the spreadsheets and probably reflected inadequate quality control. Most
of the problems related to interpretation of clinical data. It seemed that
company employees had optimistic views of their product's performance, and
analysts had to make do with suboptimal and poorly designed studies when making
inferences regarding comparative clinical performance. Complex modeling techniques
do not overcome fundamental deficiencies in clinical data.
Despite these concerns, which have been voiced in other review articles,6,8,20 the use of economic
analyses as an aid to making decisions about allocating resources in health
care is increasing. A number of jurisdictions now have published guidelines
for economic analysis, and organizations such as health maintenance organizations
and national and provincial governments are considering results of analyses
when making assessments about new pharmaceuticals and health technologies.21,22
In light of our experience, do we support the widespread use of pharmacoeconomics
in decision making? Overall our answer is yes, but this requires important
qualification. Clearly, there are many methodologic issues that need to be
considered in the evaluation of these analyses. Initially, we were surprised
at the number of submissions that had significant methodologic problems. Over
time we formed the view that this was not unexpected given the nature of the
process, which requires a complicated synthesis of data from a variety of
sources. The problems we report here had the potential to distort the estimate
of the cost-effectiveness ratio for each product and therefore the decisions
that were based on the interpretation of this parameter. It should be noted
that other factors are considered in the decision-making process, such as
clinical need, equity of access, "rule of rescue" (rule
of rescue is the desire of most societies to spend large amounts of
money to save individuals who are in extreme danger; applied to drugs, it
means accepting some cost-ineffective interventions for patients with rare
catastrophic illnesses who have no other treatment options), and the total
cost to the health care system, so that a flawed evaluation did not necessarily
lead to the drug being rejected for subsidy.
The evaluation process that identified these problems was demanding
and required (typically) close examination of the data by several staff members
with training in clinical epidemiology and health economics. Sometimes identification
of key issues required examination and reanalysis of source data; sometimes
it required examination of a spreadsheet. Because we were working within a
national program for reimbursement of pharmaceuticals (annual expenditure
>Aus $3 billion) it was possible to direct the resources needed to evaluate
these analyses fully. In many cases, the analyses were corrected to allow
a recommendation about reimbursement to be made by the parent committee.
In our view, any agency or organization that wishes to make formal use
of pharmacoeconomic data must appreciate the intensity of the evaluation process
that is necessary to ensure that decisions are based on accurate data. We
doubt whether any conventional peer-review process is adequate. Such concerns
have been expressed by others, and a recently published article and editorial
have pointed to possible bias in the qualitative conclusions in pharmacoeconomic
analyses published by academic investigators.7,23-26
Published pharmacoeconomic analyses are subject to the same constraints as
any scientific study. They have to comply with journal space requirements
that limit the presentation of relevant data. Complex models and calculations
and details of assumptions and methods have to be truncated to comply with
journal and editorial requirements. Referees are seldom provided with the
computer models, and most will not have the time or inclination to run them.
A 10-page manuscript is in stark contrast to the 2- and 3-volume (400-page)
submissions containing the details of the economic evaluations that are routinely
evaluated for the Australian Pharmaceutical Benefits Scheme.
As mentioned earlier, we do not believe that the problems identified
in the review were deliberately introduced. Most of the problems were the
result of the intrinsically complex nature of economic analyses and genuine
differences of opinion about how to interpret the results of clinical trials.
This makes it important for full details of analyses to be available to reviewers,
readers, and decision makers, so that they can make informed judgments regarding
analysis validity. In our view, conventional publication in peer-reviewed
journals is not a realistic means of meeting this requirement.
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