Real World Data Methods

Applications in Using Large Databases
Room: Oceans Ballroom 9

Faculty: Diana Brixner PhD, RPh, Professor and Executive Director, University of Utah, College of Pharmacy and Pharmacotherapy Outcomes Research Center, Salt Lake City, UT, USA; Michael Eaddy PhD, PharmD, Senior Director, Xcenda, Palm Harbor, FL, USA; John Parkinson PhD, Head of GPRD, London, UK

Course Description: This course will provide a review of three health care databases – GPRD (UK database), GE Centricity electronic medical record (USA database) and Medicaid (USA database).  Each database will be discussed in-depth including directions on how to access the information and how researchers utilize this information.  Instructors will distinguish the important differences between these databases including the limitations and strategies to maximize their value through the use of an interactive format with interactive examples. Discussion will include a reference to the ISPOR Classification of Databases Working Group / Retrospective Database Special Interest Group and its digest of international databases. Participants must have some knowledge of administrative health care database analysis.

Patient Registries
Room: Oceans Ballroom 10

Faculty: Chris Pashos PhD, Vice President and Executive Director, HERQuLES Health Economic Research and Quality of Life Evaluation Services, Abt Bio-Pharma Solutions, Inc., Lexington, MA, USA; Steven Fosburg AM, President, Abt Bio-Pharma Solutions, Inc., Lexington, MA, USA; Kay M. Larholt ScD, Vice President, Biometrics & Clinical Operations, Abt Bio-Pharma Solutions, Inc., Lexington, MA, USA; Neal S. Mantick MS, Executive Director, Observational Studies, Abt-BioPharma Solutions, Inc. (ABS), Lexington, MA, USA

Course Description: This course is designed to provide an overview of patient registries and their applications in identifying 'real world' clinical, safety, and patient-perspective issues. The pros and cons of registry data compared to other 'real world' and clinical trial data collection will be presented.  How registry information can be used to support other health economics /outcomes research initiatives and health care decision making will be addressed.  Registry strategy, design, operations and measures of program success will be discussed.  In addition, regulatory trends and requirements, including the Agency for Healthcare Research & Quality’s (AHRQ) May 2007 publication: Registries for Evaluating Patient Outcomes: A User's Guide, will be examined.  This course is designed for those with little experience with patient registries.

Modeling Methods

Bayesian Analysis: Advanced
Room: Oceans Ballroom 12

Faculty: Keith R. Abrams PhD, Professor of Medical Statistics, Department of Health Sciences, University of Leicester, Leicester, UK; Christopher S. Hollenbeak PhD, Associate Professor, Surgery and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA; David J. Vanness PhD, Research Scientist, Center for Health Economics and Science Policy, United BioSource Corporation, Bethesda, MD, USA and Assistant Professor, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Course Description: This course introduces the use of Bayesian methods in evidence synthesis (including meta-analysis) and allows participants to gain hands-on experience using such modeling techniques within WinBUGS. Methodological issues considered in the course include: fixed & random effects models, choice of prior distributions, subgroups, meta-regression and adjusting for baseline risk, together with indirect and mixed treatment comparisons. Further meta-analysis topics for which a Bayesian approach can be of benefit will also be highlighted. Participants will be expected to be familiar with the use of WinBUGS and will be responsible for bringing a laptop with the latest, unrestricted version of WinBUGS pre-installed [Details at www.mrc-bsu.cam.ac.uk/bugs ]. This course is a follow-up to the course: Bayesian Analysis-Overview and Applications. Basic knowledge of Bayesian approach and use of WinBUGS will be assumed.

Discrete Event Simulation for Economic Analyses
Room: Oceans Ballroom 11

Faculty: J. Jaime Caro MDCM, FRCPC, FACP, Adjunct Professor of Medicine, Adjunct Professor of Epidemiology and Biostatistics, McGill University, Montreal PQ & Senior Vice-President of Health Economics, United BioSource Corporation, Lexington, MA, USA; Jörgen Möller MSc Mech Eng, Vice-President, Modeling, United BioSource Corporation, Eslov, Sweden

Course Description: This course will provide a basic understanding of the key concepts of discrete event simulation. The focus will be on the use of these simulation models to address pharmacoeconomic (and device-related) problems. The course will be structured around practical exercises. Topics to be covered are: Why DES? Dynamic simulation as a tool; Components of a DES; How do you build a model? Modeling of processes and resource use; Modeling of variables and decisions. If time permits, simple animation will be demonstrated. We will use ARENA to build simple models. Instructors will distribute training versions of Arena. This course is designed for those with some experience with modeling.

Use of Pharmacoeconomics / Economic / Outcomes Research Information

Case Studies in Pharmaceutical/Biotech Pricing II – Advanced
Room: Coral Ballroom A

Faculty: Jack M. Mycka, Global President & CEO, MME LLC, Montclair, NJ, USA; Renato Dellamano PhD, President, MME Europe & ValueVector, Milan, Italy

Course Description: Case studies will be employed to lead participants through the key steps of new product pricing, with focus on the need to thoroughly analyze the business environment and its constraints and opportunities, and the need to closely integrate the pricing, reimbursement and Pharmacoeconomic strategy for the new product with the clinical development and marketing strategies. Practical exercises will allow participants to consolidate the concepts delivered in the “Elements” introductory session and expanded here. Areas covered will include the post-launch issues of reimbursement and pricing maintenance as a part of life-cycle management in a global environment. This course is for individuals who have completed Elements of Pharmaceutical Pricing I – Introduction or are familiar with both the key determinants of pharmaceutical pricing and the main international health systems.

Pharmacoeconomic / Economic Methods

Statistical Considerations in Health Economic Evaluations
Room: Coral Ballroom BC

Faculty: Henry Glick PhD, Associate Professor of Medicine, Division of Internal Medicine, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Jalpa Doshi PhD, Research Assistant Professor of Medicine, Division of Internal Medicine, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Course Description: Adoption and diffusion of new medical treatments depend increasingly on robust analysis of costs and cost-effectiveness. During this course, we discuss design issues for the collection of primary economic data as well as statistical considerations, including the effect of distributional assumptions, univariate and multivariable analyses of data, sample size and power calculations, and estimation of sampling uncertainty for cost-effectiveness analysis. Examples will be provided to illustrate concepts.  Participants should have basic knowledge of economic evaluations and statistics.

Pharmacoeconomic / Economic Methods

Applications of Statistical Considerations in Health Economic Evaluations
Room: Coral Ballroom BC

Faculty: Henry Glick PhD, Associate Professor of Medicine, Division of Internal Medicine, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Jalpa Doshi PhD, Research Assistant Professor of Medicine, Division of Internal Medicine, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Course Description: This course will provide applications of statistical considerations in economic analysis. Specific exercises will be conducted to illustrate univariate & multivariable analysis of costs (including the effect of  distributional assumptions), sample size and power calculations for cost-effectiveness analysis, and estimation of confidence intervals for cost-effectiveness ratios and net monetary benefits as well as acceptability curves.  Participants are encouraged to have hands-on experience and bring their laptops. Statistical analysis will be done by use of STATA.  A trial version of the software will be distributed for those who do not have the software. The text “Economic Evaluation in Clinical Trials” (Oxford: OUP, 2007) is recommended reading for this course. The course, Statistical Consideration in Health Economic Evaluations, is a strong prerequisite for this course.

Real World Data Methods

Outcomes Research for Medical Devices and Diagnostics
Room: Oceans Ballroom 9

Faculty: Seema Sonnad PhD, Associate Professor, Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA; Stacey Ackerman MSE, PhD, Vice President, Covance Market Access Services, San Diego, CA, USA

Course Description: This course will present outcomes research practices that are specifically tailored for the fast-paced medical device and diagnostics technology environment and address issues related to these health technology assessment methodologies. Outcomes research including clinical outcomes, economic outcomes, and patient-reported outcomes will be discussed. Outcomes research for medical devices and diagnostics will be differentiated from other health care interventions such as drugs. The evidence hierarchy for medical devices and diagnostic procedures including ‘real world’ outcomes research information in coverage and reimbursement decisions will be discussed. This course is designed for those with little experience with outcomes research for medical devices and diagnostic technologies.

Propensity Scores and Observational Studies of Treatment Effect
Room: Oceans Ballroom 12

Faculty: John Seeger PharmD, DrPH, Chief Scientist, i3 Drug Safety, Waltham, MA, USA

Course Description: A large part of the evidence about the effectiveness of different treatments is based on observational research. Issues of bias and confounding relate to study design and analysis in the setting of non-random treatment assignment where compared subjects might differ substantially with respect to comorbidities. This course will outline the key concerns in the conduct of observational research of treatment effect and explain methods for causal inference in observational settings.  With no control over the treatment assignment and the lack of balance in the covariates between the treatment and control groups can produce confounded estimates of treatment effect. We will explain how propensity scores can be used to mitigate confounding, through standard observational approaches (restriction, stratification, matching, regression, or weighting). The advantages and disadvantages of standard adjustment relative to propensity score-based methods will be discussed.  Details of propensity score methodology (variable selection, use, and diagnostics) will also be discussed. The course will also elaborate briefly on risk adjustment models that collapse predictors of outcomes (disease risk scores such as the Charlson Comorbidity Index, and Chronic Disease Scores) and their use relative to propensity scores. This is an introductory course, designed for those with little experience with this methodology but some knowledge of observational databases.

Quality of Life / Patient-Reported Outcomes / Preference-Based Methods

Patient-Reported Outcomes - Item Response Theory
Room: Coral Ballroom A

Faculty: Bryce Reeve PhD, Psychometrician, National Cancer Institute, National Institute for Health, Bethesda, MD, USA

Course Description: There is a great need in health outcomes research to develop instruments that accurately measure a person's health status with minimal response burden. This need for psychometrically sound and clinically meaningful measures calls for better analytical tools beyond the methods available from traditional measurement theory. Applications of item response theory (IRT) modeling have increased considerably because of its utility for instrument development and evaluation, assessment of measurement equivalence, instrument linking, and computerized adaptive testing. IRT models the relationship, in probabilistic terms, between a person's response to a survey question and their standing on a health construct such as fatigue or depression. This information allows instrument developers to develop reliable and efficient quality of life measures tailored for an individual or group. This introductory workshop will discuss the basics of IRT models and applications of these models to improve health outcomes measurement. Illustrations will be used throughout the presentation that focus on measuring key health-related quality of life domains in different disease populations. This introductory course is designed for those with no to little experience with IRT.

Utility Measures
Room: Oceans Ballroom 11

Faculty: Andrew Lloyd DPhil, Director, Oxford Outcomes, Oxford, UK

Course Description: This course is designed to provide an introduction and overview of utility measures to support economic evaluations.  The concepts of health-related quality of life and utility will be introduced and discussed in terms of their differences and similarities.  We will describe how these data can be combined with survival to estimate quality-adjusted life years (QALY).  QALY axioms and issues as described in the Value in Health Vol 12, Issue 2 (2009) Special issue, “Moving the QALY Forward: Building a Pragmatic Road” will be introduced.  In the second section we will explore the methods that are used to capture utilities such as standard gamble, time trade off and rating scales will be described.  Building on this will be a presentation of the different generic instruments that have been developed for measuring quality of life such as the EQ-5D, Health Utilities Index and SF-6D...  Estimating utilities from a condition specific measure will also be discussed.  In the third section we will describe approaches that can be used when utility data from trials are not available.  The development of mapping functions and other crosswalks will be described from disease specific measures to generic HRQL measures.  The pros and cons of the different main approaches will be discussed.  Other approaches to addressing a lack of utility data will also be described including prospective observational studies, systematic reviews and critical appraisal of published values and lastly the valuation of vignette type descriptions of health. In the final section we will describe the requirements and preferences of different reimbursement agencies around the world including UK/Australia/Canada; US agencies; EU markets such as Sweden/Belgium/Netherlands/Germany; Asia and Latin America.  The course will be interactive with breakout sessions and group discussion.  This is an introductory level course; no prior knowledge of utilities or health-related quality of life is assumed.

Modeling Methods

Advanced Decision Modeling for Health Economic Evaluations
Room: Oceans Ballroom 10

Faculty: Andrew Briggs DPhil, MSc, Lindsay Chair in Health Policy and Economic Evaluation Section of Public Health, University of Glasgow, Glasgow, Scotland; Mark Sculpher PhD, MSc, Professor, University of York, Centre for Health Economics, Heslington York, UK

Course Description: During this course, the key aspects and new developments of decision modeling for economic analysis will be considered. How models can be made probabilistic to capture parameter uncertainty (including rationale, choosing parameter distributions, and types of uncertainty) will be covered. How to analyze and present the results of probabilistic models will be presented. How the results of probabilistic decision modeling should be interpreted and how decisions should be made (including decisions with uncertainty, and expected value of perfect information [EVPI]), will be presented. Specific examples including Excel programming will be used to illustrate concepts. Participants should have a basic understanding of decision analysis. The course, Modeling: Design and Structure of a Model, is a strong prerequisite for this course.