WW1
USE AND MISUSE OF DIAGNOSIS RELATED GROUPS (DRGS) IN ESTIMATING COSTS
O'Brien J
Caro Research Institute, Concord, MA, USA
Learning Objectives: To provide a basic understanding of DRGs and similar
classification systems in terms of when and when not to use them as cost inputs
for economic analyses.
Who Would Benefit: Researchers who are engaged in the development of costs for
health economic models and those who need to assess the credibility of the cost
inputs used in a model.
DRGs are a successful export of the U.S. health care system in that they have
been adopted or adapted by many other countries. Australia (AN-DRGs), Canada
(CMGs), France (GHMs), Spain (PMGs) and the United Kingdom (HRGs) are but a few
of the countries that have some type of DRG system. Yet, all DRG-based systems
are not created equal and are not utilized for the same purpose; therefore, they
should not be considered interchangeable when doing multi-country economic
analyses. Although DRG-type data are often the most accessible, they are not
necessarily the optimal source for cost estimates. To understand the
implications of using DRG-based cost estimates, it is necessary to understand
the basic structure of DRGs, how cases are grouped, how case costs are derived
and assigned in each country, the cost content, and the relevance of their use
in cost analyses. This workshop will focus on describing these DRG-related
topics with particular emphasis on differences between U.S. DRGs and systems
used in other countries, as well as providing practical examples of how to best
use DRGs in economic analyses. Specific cost estimate examples will be used to
illustrate how erroneous cost estimates can result from DRG misuse and why it is
important to avoid the pitfalls of DRG use. Participants will be encouraged to
relate relevant experience with DRG use in their respective countries.
WW2 INCIDENCE OR PREVALENCE: IMPLICATIONS FOR FORMULARY DECISION-MAKING
Shaya FT1,Mullins CD1, Wong W2.
1University of Maryland, Baltimore, MD, USA; 2CareFirst BlueCross BlueShield,
Baltimore, MD, USA
Learning Objectives: This workshop will demonstrate the difference in projecting
utilization and costs using incidence versus prevalence data. The aim of the
workshop is to outline and apply the epidemiological and economic concepts
underlying the choice, and the implications for modeling and decision-making.
Who Would Benefit: Researchers who are involved in designing and implementing
pharmacoeconomic and outcomes research studies, stakeholders and decision makers
in managed care, pharmaceutical research and marketing, to the extent that they
are interested in the formulary selection process.
There is an increasing interest in modeling disease events and the short-run and
long-run economic impact of therapies on these events. The models can be used by
Pharmacy and Therapeutics (P&T) Committees to examine the budgetary impact of
proposed formulary changes. There is a current debate, however, about the issue
of whether to use a prevalence-based or an incidence-based approach in these
models. Typically, each will yield a different result, with implications for
formulary decisions and projecting budget impacts. The prevalence framework
represents the patterns of treatment experienced by the health plan over a
specified period of time, irrespective of the disease state reached by
individual members. It lends itself to modeling chronic diseases. In contrast,
acute diseases are best modeled in the incidence-based framework. This session
will describe the scientific approach to determining the appropriateness of
either framework, and will provide an interactive illustration of their
application to formulary selection. Participants will learn and apply the
epidemiological and economic concepts that underlie the decision process. With
active participation from the audience, a model will be developed for the
formulary selection of a therapy from a managed care perspective, with an
examination of the budget impact, considering alternatively an incidence and a
prevalence-based approach.
WW3 PROBABILISTIC COST-EFFECTIVENESS MODELING: OVERVIEW OF METHODS AND CHALLENGES
WITH AN INTERACTIVE ILLUSTRATION
Briggs AH1, Gagnon YM2, Levy AR3.
1University of Oxford, Oxford, UK; 2OCCAM Research & Consulting Inc, Vancouver,
BC, Canada; 3University of British Columbia, Vancouver, BC, Canada
Learning Objectives: The aim of this workshop is to provide participants with an
overview of the theory and methods used for building probabilistic
cost-effectiveness analyses. To illustrate relevant design issues and some of
the technical challenges, an interactive example based on a published Markov
model of the cost-effectiveness of ß-blockers for treating heart failure will be
presented.
Who Would Benefit: Researchers from academia and industry as well as anyone with
intermediate level knowledge of design and principles of cost-effectiveness
analyses. Some experience with Markov models is also an asset.
Incorporating uncertainty into decision analysis models through a fully
probabilistic framework is becoming an accepted standard for economic
evaluations of pharmaceuticals. This framework permits a synthesis of data from
disparate sources that can provide statistically robust results and be used by
investigators to estimate confidence intervals for incremental
cost-effectiveness ratios. The framework is well suited to other analyses that
can provide a greater understanding of the data and more intuitive methods of
presentation, including net health benefits, cost-effectiveness acceptability
curves, and value of information analysis. A short introduction outlining the
fundamentals of uncertainty analysis through probabilistic modeling will
initiate the workshop session. This will be followed by a brief presentation of
an incremental cost-effectiveness analysis of ß-blockers for treating heart
failure. An Excel spreadsheet version of the model will be used to interactively
illustrate issues such as fitting data distributions to model parameters and
estimating cost-effectiveness acceptability curves in a scenario comprising of
more than two competing treatment alternatives. Throughout the session,
participants will have the opportunity to raise issues on implementation of the
methods and elements requiring further explanation. This will enhance the
participants’ involvement in a resourceful learning experience. The session will
conclude with a short exposition of strengths and weaknesses of the techniques
and the current state of research in the field.
WW4 MEASURING PATIENT-REPORTED OUTCOMES IN CANCER STUDIES: PRELIMINARY FINDINGS FROM
THE CANCER OUTCOMES MEASUREMENT WORKING GROUP
Lipscomb J1, Gotay CC2,
Snyder C1.
1National Cancer Institute, Bethesda, MD, USA; 2University of Hawaii, Honolulu,
HI, USA
Learning Objectives: This workshop will describe the preliminary findings of the
Cancer Outcomes Measurement Working Group, including the current
state-of-the-art of measuring patient-reported outcomes in cancer and areas
requiring further improvement.
Who Would Benefit: Researchers who include patient-reported outcomes
(health-related quality of life, patient satisfaction, economic burden) in
cancer studies; findings also have applicability to other disease areas.
In response to the increasing importance of and demand for outcomes research,
the National Cancer Institute (NCI) established the Cancer Outcomes Measurement
Working Group (COMWG) to assess the state of the science of outcomes measurement
and identify priorities for future research and practice. The COMWG is part of
NCI’s Quality of Cancer Care Initiative; however, the COMWG is not an advisory
committee or decisional group. The NCI invited 35 researchers primarily from
academia, medical centers, and government to participate in the COMWG, and
members have been working over the past 18 months to provide their individual
input on the state of the science and recommendations to improve measurement of
key patient outcomes in cancer. Specifically, the COMWG is addressing three
endpoints (health-related quality of life, patient satisfaction, and economic
burden), four cancers (breast, prostate, lung, and colorectal), the continuum of
care (screening/prevention, treatment, survivorship, end of life), and multiple
applications (individual patient/clinician decision-making, clinical trials,
observational studies). The workshop will address several of the key issues
related to outcomes measurement: measurement model selection and the relative
strengths and weaknesses of currently available measures, analytic issues such
as missing data and defining a clinically important difference, needs for future
research including the role of modern psychometric theory, and implications of
outcomes research including industry and regulatory uses. Audience participation
will be encouraged through an activity that uses a case study to review
instrument selection and application and discusses how the COMWG results inform
this process.
WW5 THE ROLE OF ECONOMIC EVALUATION OF PHARMACEUTICALS IN HEALTH DEVELOPMENT
ASSISTANCE
Rovira J, Preker AS, Musgrove PA.
The World Bank, Washington, DC, USA
Learning Objectives: The objective is to make participants aware of the past and
present use of economic evaluation of drugs by development agencies, to identify
its limitations and strengths and to find ways to improve its practice and use.
Who Would Benefit: Those interested in increasing the use of economic evaluation
by decision makers in developing countries.
The issue of the accessibility of low-income countries to pharmaceuticals to
new, innovative drugs is a hot issue in the international health policy debate.
International agencies, donors and NGOs are taking action trying to ensure that
developing countries can satisfy their needs in pharmaceuticals. The World Bank
has become probably the main single source of funding for pharmaceuticals in low
and middle-income countries. The current focus of the debate is mostly on prices
and (financial) affordability, rather than on efficiency. However, since 1993
the World Bank has been recommending developing countries to use economic
evaluation a tool for priority setting, which has been in its turn advocated as
a criterion for resource allocation and priority setting. Moreover, the
authorisation of a Bank’s loan usually requires an economic evaluation to be
carried out. However, there are some concerns on the usefulness and real use of
the tool. The session coordinator will present some evidence on how economic
evaluation has been carried out and used in Bank’s loans and projects that have
a pharmaceutical component. The following topics are proposed for the workshop
discussion: Is there evidence of economic evaluation been used for decision
making in the field of development assistance? What is the scope of economic
evaluation in different forms of development assistance, such as, according
loans, making donations, and building capacity? Is economic evaluation, as it is
made and used in developed countries, useful for developing countries? What
should be changed? Can developing countries benefit from the knowledge of
developed countries in that area?
Learning Objectives: Clinical trials evaluating quality of life have long been
prone to missing data due to death, incomplete forms or drop out. This workshop
will review missing data issues, testing for missingness, and analysis
techniques to impute missing data. Examples will be provided from recent trials
and at the conclusion, attendees should be able to test for the type missing
data and understand how analysis techniques may influence results.
Who Would Benefit: This session is directed at individuals who are responsible
for the design and conduct of outcomes studies. Individuals who need to
interpret study results will also benefit from this workshop.
The increase in quality of life evaluations alongside clinical trials has
spurred the interest and necessity of missing data imputation techniques. This
workshop will review the types of missing data (missing at random, missing
completely at random and not missing at random), how to test for the influence
of missingness, and several strategies of imputing data (LOCF, complete cases,
multiple imputation, and observed data). Examples from recent clinical trials
will be used to provide a better understanding of these techniques and their
influence on results. Attendees will learn the difference between types of
missing data, how to test for their influence, and how different imputation
techniques work.
WW7 USING HEALTHCARE CLAIMS DATA TO DEVELOP DISEASE RISK PREDICTION MODELS
Russell MW1, Huse DM2.
1ICSL Healthcare Research, Waltham, MA, USA; 2PharMetrics, Inc, Watertown, MA,
USA
Learning Objectives: The purpose of this workshop is to describe how databases
of healthcare claims can be used to develop models to predict the occurrence of
specified health events (e.g. onset of a disease or acute exacerbations of an
existing disease) in a general population.
Who Would Benefit: (1) Medical decision makers in provider and payer
organizations; (2) Researchers who are interested in learning more about the
general methods of disease risk prediction and, specifically, the use of
healthcare claims data to predict disease risk.
So-called “risk prediction models” have been used for decades by clinicians and
researchers to better understand the association between an event of interest
(e.g. disease onset) and “risk factors” for that event. Probably the best known
examples of such models are those derived from Framingham Heart Study data.
While cardiovascular disease has proved to be a fruitful domain for such
modeling over the years, in many disease areas the relation between risk factors
and disease occurrence remains poorly delineated. Moreover, some decision makers
question the generalizability of existing models to their own populations of
interest (e.g. managed care plan membership). In this workshop, the use of
healthcare claims data to develop disease risk prediction models will be
explored. Topics to be covered include (1) the longitudinal scope of data
required to estimate such models, (2) estimation techniques, (3) key assumptions
that may be required, (4) other methodological issues (e.g. “surrogate markers”
for risk factors, inference of diagnostic information from ICD-9-CM diagnosis
codes), and (5) the application of these models to healthcare decision making.
The workshop will provide a case study from recent research (occurrence of
deep-vein thrombosis) as well as an audience-directed case study.
WW8 COMPARISON OF "TOP-DOWN" VS "BOTTOM-UP" BUDGET IMPACT MODELS Kleinstiver PW
Katalyst Health Technology Assessments, London, ON, Canada
Learning Objectives: Using actual drug utilization data, the two methods of
conducting budget impact analyses will be compared.
Who Would Benefit: Pharmacoeconomics personnel from government, industry and
academia.
Preparation of budget impact analyses can be conducted by making global
assumptions about new drug market share, utilization ratios and overall market
growth ("top down" approach) or by entering detailed information for these
parameters on a drug-by-drug basis (“bottom up” approach). Each method has
unique advantages and disadvantages. The global approach (“top down”) utilizes
yearly estimates of market share for the new drug along with corresponding
utilization ratio estimates (assuming the drug has more than one strength) and
an overall market growth approximation for each forecasted year. This method is
relatively simple and easily programmed on a spreadsheet using a weighted
average methodology (by strength of each drug). The primary advantages of the
“top down” approach are speed and simplicity; however the principal disadvantage
is that market share assumptions are unrealistically distributed equally against
all strengths of all competitive compounds. Additionally, global growth
estimates apply equally to all strengths of all competitive compounds, which may
be an unrealistic assumption in some cases. The detailed or “bottom up”
approach, while requiring a more complex spreadsheet or pharmacoeconomic model,
allows for entry of annual market share and growth data that can be specific to
individual strengths of competitive compounds while still maintaining the
ability to include annual utilization ratios for the new drug. Both models
provide for sensitivity analyses, the ability to generate reports with and
without generic compounds and provide surrogate variables for future competitive
entries of either generic or branded compounds. Spreadsheet and pharmacoeconomic
models with examples from several therapeutic areas, from private and public
payer markets in Canada and the United States, will demonstrate “top down” and
“bottom up” approaches and illustrate advantages and disadvantages of each
method.
WW9 INCORPORATING ECONOMIC OUTCOMES INTO FORMULARY DECISIONS
Mauskopf J1, Wright A2,
Hocker S3.
1MEDTAP International, Durham, NC, USA; 2Advance PCS,
Hunt Valley, MD; 3PAREXEL, Baltimore, MD, USA
Learning Objectives: The objectives of this workshop are: 1. To review the
status of integration of the Guidelines 2. To discuss the benefits and
challenges posed by the Guidelines from the perspective of decision makers 3. To
interactively address practical examples of how the use of the Guidelines might
impact decision making in, for example, the case of upcoming patent expirations.
Who Would Benefit: Formulary decision makers from healthplans and PBM's Non-US
formulary decision makers desiring to keep current with the changes in US
practice Health economics, contracting, and marketing personnel from
manufacturers who are interested in input from PBM's and healthplans on the
status of integrating the guidelines into decision making.
The Academy of Managed Care Pharmacy is following the path set by Australia,
Canada, and many European countries as well as Regence Blue Cross/Blue Shield in
a current initiative designed to encourage healthplans to request that safety
and efficacy data be supplemented by patient-reported outcomes and economic
evaluations. AMCP’s initiative represents a first step in the US to include a
broader range of disease outcomes in formulary decision making. Adoption of the
AMCP Guidelines offers both benefits and challenges to plans to incorporate the
guidelines into decision making. The status of adoption as well as the benefits
and challenges from both a researcher and decision-maker perspective will be
discussed in an interactive setting. The discussion will be structured around a
series of case studies illustrating different ways of presenting outcomes and
economics data to the decision-maker, different data sources for the
information, and different decision-making contexts. For example, incidence and
prevalence based outcomes in the analysis of schizophrenia data and the impact
of perspective on conclusions will be discussed as well as how upcoming patent
expirations should be factored in by the decision-maker.
WW10 NUMBER NEEDED TO TREAT (NNT): IS IT A USEFUL BENCHMARK FOR THE EFFICIENCY OF
THERAPIES?
Caro J1, Huybrechts K1, Kamae I2.
1Caro Research Institute, Concord, MA, USA; 2Kobe University, Chuou-ku, Kobe,
Japan
Learning Objectives: Evaluate the usefulness of the Number Needed to Treat (NNT)
— the number of patients who must be treated in order to prevent one adverse
event — as a simple summary statistic to compare the efficiency of various
interventions.
Who Would Benefit: Researchers interested in learning more about the NNT concept
which has been referred to as the “currency of Evidence Based Medicine” and its
potential role in economic evaluations.
The NNT — the reciprocal of the absolute risk reduction — was introduced in 1988
as an “easily understood yardstick to describe the harm as well as the benefit
of therapy and other clinical maneuvers”. Although presented as a tool to
facilitate clinical decision-making, its use in public decision-making was
already hinted at in this first publication. The NNT has since been portrayed as
a first approximation to more complex measures such as cost-utility ratios,
which are considered the ultimate goal of Evidence Based Medicine, and NNT-based
league tables have been presented. In this workshop we will review the origin
and properties of the NNT and evaluate whether it indeed meets the criteria of a
useful decision-making tool: comparative, easy to understand and calculate,
standardized. Although the first three criteria appear to be met, we will
demonstrate how the modifications proposed over the years to address the
shortcomings of the original NNT in terms of standardization have greatly
increased its complexity while major issues of standardization remain. We will
also introduce a new odds method to address the common misinterpretation that
treating the number of patients implied by the NNT will “certainly” — instead of
“statistically” — prevent one adverse event. Throughout this critical evaluation
phase, direct input from the workshop participants will be solicited. We will
conclude by presenting our approach to calculate an “adjusted” NNT for public
health use.
WW11 THE APPLICATION OF EXPERT OPINION IN PHARMACOECONOMIC STUDIES
Evans C, Crawford B
Mapi Values, Boston, MA, USA
Learning Objectives: The use of expert opinion in pharmacoeconomic studies does
not follow any consistent pattern. As there is little consistency, it is
important for researchers to understand the relative strengths and shortcomings
of data collection methods that rely on expert opinion and to critically
evaluate studies that utilize expert opinion.
Who Would Benefit: This session is directed at individuals who are responsible
for the design and conduct of pharmacoeconomic evaluations. Individuals who need
to interpret study results will also benefit from this workshop.
Expert opinion is used frequently in early phase development, decision analytic
models and in disease management. There are various techniques to choose from
when a researcher is interested in obtaining information from a panel of
experts. Round tables, nominal group techniques, modified Delphi and Delphi
panels are the most common. Unfortunately there is little consistency in the way
that expert opinion is obtained or used. This workshop explores the use of value
panels, nominal group processes, modified Delphi panels and Delphi panels in
pharmacoeconomic research. Current guidelines are reviewed to assess the use of
this information by reimbursement authorities in European, Canadian and
Australian settings. Each technique is described as well as a recommendation as
to when it can be appropriately employed in pharmacoeconomic research. In
addition, information is provided to participants as to areas where mistakes are
most likely to arise and how to avoid deriving misleading information. The
workshop covers several key areas: the provision of baseline information, the
attrition of experts, the criteria for selecting experts, the definition of
consensus, the appropriate use of terms and the overall validity of this type of
data. In addition, for processes that aim to gain a consensus of opinion, the
areas of stability and convergence are reviewed and guidance is given on how the
use of expert opinion should be reported in pharmacoeconomic studies.
WW12 THE USE OF PATIENT SELF-REPORTED HUMANISTIC OUTCOMES INFORMATION TO IMPROVE
QUALITY OF CARE
Schmeichel CJ, LeVine P, Netherton D
InfoMedics, Inc, Woburn, MA, USA
Learning Objectives: 'Naturalistic' outcome studies enable the researcher to
better understand patient responses to medical interventions through the use of
the 'real-world' setting as compared with the limitations imposed through use of
a strict clinical protocol. This workshop will describe the broader utility of
'real-world' outcomes data, potentially as a means to improve the quality of
medical care and the perception of therapeutic effectiveness on the part of
patient and physician.
Who Would Benefit: Clinical outcomes researchers; health economists,
pharmacoepidemiologists; clinicians; benefit managers; stakeholders in
consumer-based medical marketing initiatives.
A ‘naturalistic’ study is one designed to collect data for the purpose of
evaluating on-going clinical effectiveness and the practical impact of medicines
in a real-world setting. The focus of a ‘naturalistic’ study is the practice of
medicine, meaning both the patient’s experience of the effectiveness of the
therapy as well as the treating physician’s patient management practices.
Naturalistic studies demonstrate a capacity to support patients in driving
quality of care and enhancing relationships between patients and physicians. By
providing their physicians information about their out-of-office ‘experiences’
with prescribed medications naturalistic studies provide 'net-new' information
that contributes to changes in patient management and improved patient outcomes.
Key to meaningful communication is the patient’s sense of privacy and security
of communications, timeliness of reporting data to the physician, and a
mechanism for patients and physicians to review the ‘between-office-visit’
information. This workshop will describe an automated system that enables
patients to communicate their experience to their treating physician. The
session will also demonstrate that a patients’ perception of wellness or
improvement communicated confidentially influences physician patient management
practices and enhances key elements of medical care. Workshop participants will
experience, first-hand, the network for confidential communication of personal
medical information. Case studies and an assessment tool will be presented for
discussion.
WW13 THE SEER-MEDICARE DATABASE: A UNIQUE RESOURCE FOR PHARMACOECONOMIC RESEARCH IN
ONCOLOGY
de Lissovoy G1, Warren JL2.
1MEDTAP International, Bethesda, MD, USA; 2National Cancer Institute, Bethesda,
MD, USA
Learning Objectives: (1) Describe the potential use of SEER-Medicare data for
pharmacoeconomic research; (2) Identify methodological and data quality issues
that may influence study design and feasibility.
Who Would Benefit: Researchers who are investigating the epidemiology, treatment
patterns, resource use, or costs associated with specific types of cancer in the
Medicare-eligible population.
The SEER (Surveillance-Epidemiology-End Results)-Medicare database is a
collaborative effort of the National Cancer Institute (NCI), the SEER Tumor
Registries, and the Centers for Medicare and Medicaid Services (CMS), to create
a large population-based source of information for health services research. The
database links detailed clinical, demographic and cause of death information
from tumor registries with Medicare enrollment and claims data. In Part 1 of
this workshop, the SEER-Medicare database will be described in terms of sources
and types of available data (types of cancer, population and geographical
coverage, time frame of available data), the structure of files (clinical,
Medicare claims, socio-economic) and the main variables of interest to
pharmacoeconomic researchers. In Part 2, a case study will be presented based on
a recent analysis of treatment patterns and costs in non-small cell lung cancer.
This case will illustrate methods used to construct an episode of care and to
establish treatment costs while controlling for survival. Limitations of
available data that should be considered in study design and interpretation of
findings will be noted. Personnel and computing resources needed to work with
these complex data will be identified. In Part 3, Federal policy on use of the
SEER-Medicare database will be reviewed, together with application procedures
for requesting access to the data. Questions and comments from the audience will
be invited during each part of the workshop.
Learning Objectives: The objective of this workshop will be to discuss how
future events should be incorporated into cost-effectiveness models for chronic
diseases
Who Would Benefit: Analysts or decision-makers involved in the conduct or
evaluation of pharmacoeconomic studies.
Cost-effectiveness analysis of an intervention for a chronic condition requires
estimates of cost and efficacy over the rest of the patients’ lifetime. However
efficacy estimates are commonly derived from clinical trials with limited
duration of follow-up. This creates the need for modeling to estimate cost and
efficacy beyond the follow-up period. Most such models do not account for future
events such as availability of generics or introduction of new treatments. Some
of these events such as generic introduction are certain, while others such as
availability of new treatments are uncertain. We will discuss the need to model
the impact of such events in cost-effectiveness analysis. For example,
cost-effectiveness assessments of early HIV therapies that did not account for
the potential introduction of future treatments that extend life may have
resulted in less than optimal allocation decisions. We will discuss how to
include both certain and uncertain events into economic models of new treatments
for chronic diseases. We will provide examples where future events could have
either a positive or a negative impact on the cost-effectiveness of the product
of interest. We will conclude the workshop with an interactive discussion
regarding the need to include future events in economic models as well as the
relative merits of the different methods for modeling the impact of such events.
Learning Objectives: The purpose of this workshop is to discuss methodological
and practical issues for designing and analyzing oncology models. The workshop
will focus on: (1) types of models and model structures suitable for evaluating
cost-effectiveness of oncology products, (2) common components in oncology
models, and (3) clinical and analytical issues to be considered.
Who Would Benefit: Researchers in academia and industry who employ
pharmacoeconomics to evaluate treatments for oncology patients, as well as
decision makers involved in reimbursement and pricing decisions.
Oncology models can be categorized into two types: chemotherapy treatments
versus medications for a particular health condition in oncology patients (e.g.,
unfractionated heparin for venous thromboembolism). Modelers need to determine
whether a Markov structure is necessary for the type of model examined. If it
is, shall it be a Markov-chain or a semi-Markov model? Depending on the line of
treatment, modelers need to decide whether information provided in the trial is
sufficient to make long-term projections. If not, what additional information is
required? Modelers should ensure that model timeframe, disease staging and
progression are consistent with cancer epidemiology. Additionally, cycle length
(if a Markov model) and management of each adverse event should be in line with
oncology practice. The coordination between researchers who design the model and
those who collect utilities of health states for oncology patients is extremely
important; a poorly coordinated study may cause modelers to make modifications
that compromise the rigor of the model. Lastly, the workshop will also discuss
how to obtain/calculate parameter values from detailed trial report when
patient-level trial data is not available. A non-small cell lung cancer model
and a breast cancer model will be used to facilitate discussions in the
workshop.
WW16 BENEFITS OF A SIMPLE DECISION ANALYTIC APPROACH TO CLINICAL DRUG DEVELOPMENT
Richter A
RTI Health Solutions, Research Triangle Park, NC, USA
Learning Objectives: This workshop will focus on a decision analytic risk
assessment strategy for answering the question, “What is the value of obtaining
additional information in a clinical development program?”
Who Would Benefit: Decision makers, both clinical and commercial, and anyone
with an interest in the drug development decision making process.
At a recent clinical development team meeting for a developmental compound, a
proposal was made to include a measure of “Days of Normal Functioning” in the
trial. This measure has not been included in previous trials, but is believed to
be of interest. However, the trial director states “We already have four
positive clinical outcomes. Why would we want to add a fifth when we are not
sure whether or not it will be positive?” A simple decision tree approach will
be presented. “Days of Normal Functioning” may or may not be collected, however
if collected, there are three possible scenarios: more days of normal
functioning than competing medications, no difference between medications, and
fewer days of normal functioning. The future market share associated with each
possible decision and consequences needs to be estimated as does the probability
of a successful scenario. Given differences in market share for each scenario, a
threshold value for the probability of success can be established. Given a
probability of success, a threshold difference in market share between scenarios
can also be established. Both the cost of the studies and the cost of developing
the claim need to be included in the assessment. In this workshop we will
examine several examples to illustrate the relationship between variables and
the value provided by having additional information. By using estimates of
probability of success and impact on the total market size, we will be better
able to assess the risk/benefit of including this additional claim in the
clinical trial program.
WW17 DECISION ANALYSIS IN THE DRUG DEVELOPMENT PROCESS: USE OF PHARMACOECONOMICS TO
INFORM “GO/NO-GO” DECISION MAKING
Thompson D1, Bird A2,
Weinstein MC2.
1Innovus Research, Inc, Medford, MA, USA; 2Harvard School of Public Health,
Boston, MA, USA
Learning Objectives: Increasingly, the marketplace success of new medical
interventions depends on their perceived value for money relative to competing
products, yet the use of pharmacoeconomic tools and techniques to determine
which products get brought to market remains limited. The purpose of this
workshop is to demonstrate how techniques of decision analysis, including
threshold analysis and value of information (VOI) analysis, can inform decisions
regarding whether to proceed with the development of a new drug.
Who Would Benefit: Researchers with training in basic decision analysis who want
to gain knowledge and experience with the advanced topics of threshold analysis
and VOI analysis, and industry representatives who want to learn how to
incorporate decision analysis and pharmacoeconomic benchmarking criteria into
product development decisions.
The decision to invest in new product development is not a minor one. Before
committing millions of dollars into basic research and clinical trial programs,
it would be useful for manufacturers to have an understanding of how much more
favorable a new agent’s efficacy and safety profile must be in order for it to
compete with existing therapies. Threshold analyses enable one to identify a
priori the range and combinations of key parameters (e.g. efficacy, adverse
event rates) that will result in favorable comparisons with competing agents
with respect to cost, effectiveness, or cost-effectiveness. They can also
provide targets for clinical researchers to aim for in the development process.
Although precise estimates of an agent’s efficacy or safety profile are
unavailable at this stage, probability distributions over the plausible range of
these parameters can be estimated from preliminary data and expert judgment. VOI
analysis then can be used to assess whether the expected benefit from investing
research dollars into clinical trials that reduce the uncertainty surrounding an
agent’s efficacy and/or safety exceeds the cost of such trials. This workshop
will describe the motivation for the use of decision analysis in new product
development decisions. The methodology for the conduct of threshold and VOI
analyses will be presented and participants will walk through an application of
these techniques for a hypothetical product.
WW18 EVIDENCE OF THERAPEUTIC EQUIVALENCE: IMPLICATIONS FOR ECONOMIC ANALYSES
Ortiz MS, Neighbors DM, Irish W
RTI Health Solutions, Durham, NC, USA
Learning Objectives: To review the guidance on equivalence studies to address
issues affecting the use of evidence of therapeutic equivalence and then to
discuss the acceptability of therapeutic equivalence arguments in economic
evaluations.
Who Would Benefit: Anyone preparing economic evaluations using clinical trial
data.
In economic evaluation, emphasis is placed on establishing relative clinical
effectiveness before moving on to the cost component of the cost-effectiveness
question. If two pharmaceuticals are considered “equivalent” they will be
subsequently compared on the basis of cost alone. Treatments may be considered
equivalent on the basis of a single efficacy measure or when they simply failed
to show a statistically significant difference due to an underpowered or a
poorly designed clinical study. In this session, bioequivalence (BE) study
guidance documents (CDER, FDA, 2000, and CPMP, EMA, 2001) will be contrasted
with a clinical trial superiority and non-inferiority guidance (CPMP, EMA,
points to consider document 2000). BE and TE guidance documents are similar in
that they both argue that equivalence studies must be designed to confirm the
absence of a clinically meaningful difference between treatments. A margin of
clinical equivalence (∆) is defined as the point where any larger difference
would matter in clinical practice. Demonstration of equivalence requires that
the treatment difference confidence interval be contained entirely with the -∆
to +∆ interval. It is important not to confuse equivalence with nonsignificance.
Differences from non-inferiority studies and superiority studies failing to show
a statistically significant difference in outcome will be discussed. In contrast
to TE guidance, BE guidance includes instructions for assessing effects on
multiple relevant outcomes before concluding true equivalence. Examples will be
presented, where safety and tolerability are key to the demonstration of value
of a drug. Opinions surrounding the acceptability of TE based arguments using a
single outcome variable will be sought.
