Quality of Life/Patient-Reported Outcomes/Valuation Issues 
 

WW2	CONJOINT ANALYSIS IN REAL TIME: USING STATED-PREFERENCE ESTIMATES TO INFORM DRUG-DEVELOPMENT DECISION MAKING

Johnson R, Hauber B, Research Triangle Institute, Research Triangle Park, NC, USA

Learning Objectives: Participants will learn i) the advantages and disadvantages of the conjoint-analysis approach to eliciting patient and physician preferences, and ii) how to analyze and interpret the results for health economics applications.
Who Would Benefit: Applied researchers or research sponsors interested in using conjoint analysis either as a means of predicting the demand for health care interventions or for valuing the benefits of treatments in preference or monetary terms.
Workshop Description: Conjoint analysis (CA) is increasingly being used by health economists and outcomes researchers as a method for obtaining patients', physicians', and other decision makers' stated preferences. These preference estimates are used to value treatment processes and outcomes in preference, utility or monetary terms. CA estimates provide more information about preferences than conventional QALY weights. They thus can be used to answer a number of questions about how various treatment processes and outcomes contribute to patient satisfaction. They also can be used to understand and guide physicians' therapeutic decisions. The objective of this workshop is to introduce participants to the uses of CA measures for informing drug-development decision-making. Using data collected during the conference from a sample of attendees, participants will learn how to derive, interpret, and use CA preference measures. Participants will first evaluate and critique the design and administration of the survey instrument. We will then demonstrate estimation using appropriate statistical techniques and widely available statistical packages, assess the quality of the resulting estimates, and evaluate the information obtained. Participants will then individually complete a series of exercises to translate coefficient estimates into relative utility weights, rates of substitution, preference shares, and value to patient, as well as display the results for ease of interpretation. The workshop will conclude by discussing how each of these measures can be used to inform decision-making.

Marquis P1, Trudeau E2, Keininger D1, 1Mapi Values, Boston, MA, USA; 2Mapi Values, Lyon, France

Learning Objectives: 1) To review the concepts of customer and patient satisfaction 2) To better understand how to measure and validate patient satisfaction questionnaires 3) To present the health authorities requirements in regard to patient satisfaction assessments.
Who Would Benefit: Outcomes researchers involved in analysis of observational or experimental clinical studies, in quality of care and in disease management.
Workshop Description: Patient satisfaction is a promising Patient Reported Outcome (PRO) since satisfaction is a predictor of positive attitudes and intentions to comply. In turn, this adds to the overall clinical effectiveness of the treatment. However, the use of satisfaction data to its full potential requires a reliable and valid measure developed with an understanding of the concepts of satisfaction such as expectations, performance and conformity to expectations. Indeed other parameters can influence the content of the questionnaire like the study design, the presence of a baseline measure, the type of comparator, patient's experience with or without the study medication. During the workshop, the model of patient satisfaction will be presented and discussed. The emphasis will be on the presentation of concrete examples of satisfaction instruments developed for observational and clinical studies. Results of the psychometric validation and of the determinants of satisfaction will be presented. Finally, Health Authorities guidelines in regard to satisfaction will be presented.

Thompson D1, Bogard E1, Weinstein MC2, 1Innovus Research, Inc, Medford, MA, USA; 2Harvard School of Public Health & Innovus Research, Inc, Boston, MA, USA

Learning Objectives: To present issues for consideration when choosing between traditional Markov cohort modeling versus microsimulation modeling, and to illustrate the process of developing and using a microsimulation model for a chronic disease in which compliance is highly variable and a key determinant of health outcome and cost.
Who Would Benefit: Researchers with a basic knowledge of the mechanics of state-transition models who wish to clarify their understanding of the relative advantages and disadvantages of deterministic versus probabilistic modeling.
Workshop Description: Probabilistic microsimulation models are an important tool for estimating the health consequences and economic costs associated with alternative clinical strategies in a population of interest. In comparison to the more common deterministic population (or "cohort") models, which allow proportions of a patient cohort to transition among health states over time, probabilistic microsimulation models replicate individual patient histories in a random fashion according to specified probability distributions. The results of a microsimulation model can inform decision makers by estimating not only mean outcomes in large numbers of patients, but also the variance of outcomes in finite panels of patients, such as those that might be seen in a mid-sized medical practice, hospital, or managed-care organization. This workshop will explore key factors for consideration when choosing a microsimulation design, including the audience of interest, "memory" requirements of the model, the use of dynamic structures, the need for sensitivity analyses, and computing speed. It will also discuss the advantages and disadvantages of microsimulation versus deterministic models as well as the appropriate presentation and interpretation of microsimulation results. These issues will be illustrated using a microsimulation model developed to assess the cost-effectiveness of alternative drug therapies for a chronic degenerative disease. Discussion will draw on the experience of the presenters and workshop participants.

Kevin M1, Kemp R2, Glass H1, 1University of the Sciences in Philadelphia, Philadelphia, PA, USA; 2Pharmacia Corporation, Peapack, NJ, USA

Learning Objectives: In relation to the body of economic theory, PharmacoEconomics relies on "economic evaluation" methods, developed upon the theoretical foundation of neo-classical (welfare) economics. Several modern economists have argued that classical economics does not provide a sufficient methodology upon which to model modern technological advancement occurring within today's Knowledge Economy. We will address the question, "Would the application of Modern Economic Theory improve the practice of PharmacoEconomics?" We suggest and describe the application of other theories and methods that may prove to be useful.
Who Would Benefit: Health Economists and other professionals who apply economic theory to develop pharmaceutical policy or inform corporate strategy.
Workshop Description: Over the past hundred years, the pharmaceutical industry has successively adapted to advances in Biology and Information Technology and has developed an ability to evolve in response to new scientific knowledge. Challenges facing the pharmaceutical industry today will require a rethinking of resource management if companies are to continue to provide revenue growth, profitability, and deliver constant shareholder return on investment. The development of other theories [New Growth Theory (1), Neo-Schumperterian Economics (2), Institutional Economics (3), and Evolutionary Economics (4)] has largely been neglected by the discipline of Pharmaceutical Economics even though this body of knowledge may better describe the competitive dynamics of the developing value driven market as compared to a historically product driven market. Our current post-genomic era is witnessing the "creative destruction" of the traditional pharmaceutical development model, we will examine how the Knowledge-Based Economy is encouraging monopolistic competition in which firms compete with one another based upon their monopoly position for a particular differentiated value and the insights that Modern Economic Theory can provide to describe this "organic growth".

Benner JS1, Cramer JA2, Rubin D3, 1ValueMedics Research, Inc, Arlington, VA, USA; 2Yale University School of Medicine, West Haven, CT, USA; 3Adheris, Inc., Woburn, MA, USA "

Learning Objectives: Improving compliance with prescription medications is a difficult task, and many intervention programs fail. This workshop will review the characteristics of successful and unsuccessful compliance interventions, implications for program design, and appropriate methods for program evaluation. Several recently published compliance interventions in various therapeutic areas will be discussed. At the conclusion of the workshop, attendees will understand the core principals for designing an evidence-based compliance intervention strategy and evaluation plan.
Who Would Benefit: This session is directed at individuals engaged in the design and/or evaluation of compliance interventions. Anyone with an interest in improving compliance in clinical or research settings will benefit from the workshop.
Workshop Description: Compliance with prescribed medications is critical to valid clinical studies and optimal therapeutic outcomes, yet noncompliance is a widespread problem with adverse consequences for patients, providers, health systems, and the pharmaceutical industry. Hundreds of compliance intervention programs have been developed and fielded, often with disappointing results. In this workshop, attendees will learn (a) five common pitfalls in the design and evaluation of compliance programs, (b) how to develop an evidence-based intervention strategy, and (c) how to appropriately measure the outcomes of such programs. The workshop format will include case studies of successful and unsuccessful interventions, where participants will discuss design elements and evaluation methods that contributed to each program's outcome. Attendees will also have the opportunity to participate in the development of a hypothetical compliance intervention program, ask questions, and share their own experiences with compliance intervention programs.
 

Caro J, Huybrechts K, Caro Research Institute, Concord, MA, USA

Learning Objectives: To review the case-control methodology and to critically evaluate its proper use and its usefulness in economic evaluations.
Who Would Benefit: Researchers involved in conducting health economic analyses, as well as those responsible for evaluating such analyses.
Workshop Description: A growing number of economic studies appearing in the literature are said to be based on case-control methodology. Upon closer examination, however, most of these are not case-control studies at all: they are in fact mislabeled cohort studies. In these studies, individuals with a given illness (incorrectly defined as "cases") are followed over time and the outcome of interest — often, total medical cost — is compared to that of those without the illness (incorrectly defined as "controls"). The net cost of the illness is estimated as the difference in cost. A proper case control study involves cases defined by the outcome of interest (e.g., patients who are admitted to a nursing home), while controls are a sample of the population from which the cases came (e.g., patients in the catchment area for those nursing homes) taken to provide estimates of the denominators for the rates of interest (e.g., patients with Alzheimer's Disease and patients without this diagnosis). Although case control studies are typically more prone to bias than cohort studies, properly conducted they are enormously efficient, particularly when the outcomes sought are rare. During this workshop, we will present the case-control methodology and highlight the differences with cohort studies. We will also review the circumstances where this methodology can be useful in the context of economic evaluations. Throughout the workshop, the issues will be illustrated by means of interactive group exercises and views of the participants will be vigorously elicited.
 

Levy AR1, Briggs AH2, Gagnon YM3, 1University of British Columbia, Vancouver, BC, Canada; 2University of Oxford, Oxford, UK; 3Occam Research & Consulting, Vancouver, BC, Canada

Learning Objectives: The aim is to provide an overview of issues related to subgroup analyses in epidemiological studies and to demonstrate how misinterpretations can lead to biased or spurious results in cost-effectiveness analyses. To identify the relevant design issues and provide guidance on how to identify appropriate and inappropriate sub-group analyses, an interactive illustration based on the literature will be presented.
Who Would Benefit: Researchers from academia and private industry and anyone with intermediate level knowledge of randomized trial design and analyses as well as principles of cost-effectiveness analyses. An understanding of effect-modification is an asset.
Workshop Description: Researchers conducting cost-effectiveness analyses often seek sub-groups among whom incremental cost-effectiveness ratios are more favorable. Typically, these analyses are derived from epidemiological studies - often randomized trials - and based on relative measures such as odds ratios or relative risks. However, cost-effectiveness ratios are based on additive measures such as risk differences or life-years gained. One consequence of this shift is that even when the epidemiologic evidence does not indicate sub-group effects on a relative scale, there are potentially important differences in absolute estimates of effectiveness, and resultant cost-effectiveness, between sub-groups. Thus, understanding the relationship between effect-modification and the scale of measurement is integral to correctly interpreting sub-group analyses. We will start by introducing fundamental notions of effect-modification and implications for cost-effectiveness. We will then examine age as an effect-modifier for risks of cardiovascular disease based on the Framingham study. Handouts will be used to illustrate interactively the choice between relative and additive models. This approach enhances participants' involvement and creates a useful learning environment. We will conclude with an exposition of potential pitfalls in misinterpreting sub-group analyses and resultant spurious cost-effectiveness results.
 

Gordon MJ, Trotter JP, Ovation Research Group, Highland Park, IL, USA

Learning Objectives: Observational outcomes studies, or registries, present a novel way of collecting naturalistic, post-approval outcomes data associated with a particular disease or product. This workshop will review the key elements of a successful registry, why and when a registry should be implemented, and how the processes employed and the information collected can address both clinical and marketing agendas.
Who Would Benefit: This workshop is intended for individuals who want to learn more about the design and usefulness of a registry, as well as how data collected from such a program can be utilized to understand the performance of a product. Outcomes researchers responsible for designing and coordinating real-world research initiatives are the primary audience. Individuals responsible for supporting post-approval safety surveillance or commercialization of a product would also benefit.
Workshop Description: Increasing costs and safety concerns have created a demand for information above and beyond what is collected in clinical trials. At the same time, gaining marketplace acceptance and disseminating information to practicing physicians are parallel goals. Registries are an excellent opportunity to collect post-approval product information while addressing other strategic objectives. This workshop will discuss the fundamentals of a well-designed registry. A case study will be presented to highlight the different stages of a registry (initial planning and organization, materials production, site selection, recruitment and training, and program implementation). In addition, the workshop will cover the timing of and rationale underlying a registry relative to more traditional Phase IV studies, and what the benefits and limitations of a registry can be. Registries employ a variety of methods for collecting real-world, naturalistic data; those in attendance will leave with a better understanding of how data can be collected, and how such information can be transformed into useful insights into a product's clinical, economic, and humanistic value.
 

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