Patient-Centered Outcomes: Focusing on the Patient

Short Course Program

SUNDAY, MAY 19, 2013 - ALL DAY COURSES | 8:00 AM – 5:00 PM
8:00 AM – 5:00 PM
Room: Grand Ballroom B (5th Floor)
Statistical Considerations in Health Economic Evaluations
Room: Grand Ballroom B (5th Floor)
Track: Economic Methods
Level: Intermediate. Participants should have basic knowledge of economic evaluations and statistics.
Faculty:
Henry Glick, PhD

Henry Glick, PhD, Associate Professor of Medicine, Division of Internal Medicine, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Sean Sullivan, PhD, MSc, RPh

Jalpa A. Doshi, PhD, Associate Professor of Medicine; Director, Economic Evaluations Unit, Center for Evidence-based Practice; Director, Value-based Insurance Design Initiatives, Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA

Course Description:

Adoption and diffusion of new medical treatments depend increasingly on analysis of costs and cost-effectiveness. During this course, we discuss design issues and applications of 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. Participants will be guided through applications of statistical considerations in economic analysis using specific hands-on exercises.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. Participants who wish to have hands-on experience must bring their laptops.

The text “Economic Evaluation in Clinical Trials” (Oxford: OUP, 2007) is recommended reading for this course.
SUNDAY, MAY 19, 2013 - MORNING COURSES | 8:00 AM – 12:00 PM

8:00 AM – 12:00 PM

Room: Grand Chenier (5th Floor)
Discrete Event Simulation for Economic Analyses – Concepts
Track: Modeling Methods
Level: Introductory. This course is designed for those with some familiarity with modeling.
Faculty:
Jaime Caro J. Jaime Caro, MDCM, FRCPC, FACP, Adjunct Professor of Medicine, Adjunct Professor of Epidemiology and Biostatistics, McGill University, Montreal PQ, Canada
Jörgen  Möller

Jörgen Möller, MSc Mech Eng,Vice-President, Modeling, United BioSource Corporation, Hammersmith, UK and Associate Researcher, Division of Health Economics, Faculty of Medicine, Lund University, Sweden

Denis  Getsios

Denis Getsios, Senior Director, Senior Research Scientist, United BioSource Corporation
Lexington, MA, USA

Course Description: This course will provide a basic understanding of the key concepts of discrete event simulation. DES topics to be covered are: how does DES work; what are the components; where is it used; for which problems is DES well suited; what are the advantages and disadvantages of DES; PSA as a simple task. The focus will be on the use of these simulation models to address pharmacoeconomic (and device-related) problems. Faculty will also discuss the recently published ISPOR-SMDM guidelines on DES.

8:00 AM – 12:00 PM

Room: Nottoway (4th Floor)
Risk-Sharing/Performance-Based Arrangements for Drugs and Other Medical Products
Track: Use of Pharmacoeconomic / Economic / Outcomes Research Information
Level: Intermediate.
Prerequisite: It will be helpful for individuals taking this course to have completed the short course, “Elements of Pharmaceutical/Biotech Pricing I – Introduction”, or to be familiar with both the key determinants of pharmaceutical pricing and the main international health systems.
Faculty:
Lou Garrison Lou Garrison, PhD, Professor, Pharmaceutical Outcomes Research & Policy Program, Department of Pharmacy, University of Washington, Seattle, WA, USA
Adrian Towse, MA, MPhil Adrian Towse, MA, MPhil, Director, Office of Health Economics, London, UK

Josh Carlson, PhD, Assistant Professor, Pharmaceutical Outcomes Research & Policy Program, Department of Pharmacy, University of Washington, Seattle, WA, USA

Course Description: There is significant and growing interest among both the payers and producers of medical products for arrangements that involve a “pay-for-performance” or “risk-sharing” element. These payment schemes involve a plan by which the performance of the product is tracked in a defined patient population over a specified period of time and the level of reimbursement is tied by formula to the outcomes achieved.  Although these agreements have an intrinsic appeal, there can be substantial barriers to their implementation. Theory and practice, including incentives and barriers, will be analyzed along with several examples of performance-based schemes from Europe, the United States, and Australia. A hypothetical case study will be used in an interactive session to illustrate a systematic approach to weighing their applicability and feasibility.

8:00 AM – 12:00 PM

Room: Oak Alley (4th Floor)
Advanced Decision Modeling for Health Economic Evaluations
Track: Modeling Methods
Level: Advanced. Participants should have an understanding of decision analysis.
Prerequisite: Previous attendance at the short course “Modeling: Design and Structure of a Model” – or equivalent knowledge – is required.
Faculty:
Andrew Briggs, DPhil, MS Andrew Briggs, DPhil, MSc, William R Lindsay Chair of Health Economics, Health Economics & Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK
Mark Sculpher, PhD, MSc Mark Sculpher, PhD, MSc, Professor of Health Economics, Centre for Health Economics, University of York, 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, 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.

8:00 AM – 12:00 PM

Room: Bayside B (4th Floor)
Bayesian Analysis – Advanced
Track: Modeling Methods
Level: Advanced.
Prerequisite: Previous attendance at the short course “Bayesian Analysis – Overview and Applications” – or equivalent knowledge – is required.  Basic knowledge of the Bayesian approach and use of WinBUGS will be assumed.
Faculty:
Keith Abrams, PhD Keith Abrams, PhD, Professor of Medical Statistics, Department of Health Sciences, University of Leicester, Leicester, UK
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]. Participants who wish to have hands-on experience must bring their laptops.

8:00 AM – 12:00 PM

Room: Bayside A (4th Floor)
Applications in Using Large Databases
Track: Observational Data Methods
Level: Intermediate.  Participants must have some knowledge of administrative health care database analysis.
Prerequisite:

Previous attendance at the short course “Introduction to Database Analysis of Observational Studies of Treatment Effects” (or equivalent knowledge) is recommended.

Faculty:
Diana Brixner, PhD, RPh Diana Brixner, PhD, RPh, Professor and Executive Director, University of Utah, College of Pharmacy and Pharmacotherapy Outcomes Research Center, Salt Lake City, UT, USA
John Parkinson, PhD

John Parkinson, PhD, Director, CPRD (Clinical Practice Research Datalink), London, UK

Michael Eaddy, PhD, PharmD Michael Eaddy, PhD, PharmD, Vice President, Xcenda, Palm Harbor, FL, USA
Course Description: This course will provide a review of three health care databases – CPRD (UK database), GE Centricity electronic medical record (USA database) and Medicare (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. The ISPOR International Digest of Databases and its use in identifying health care databases around the globe will be briefly discussed.  

8:00 AM – 12:00 PM

Room: Grand Ballroom E (5th Floor)
Patient-Reported Outcomes – Item Response Theory
Track: Patient-Reported Outcomes / Preference-Based Methods
Level: Introductory. This course is designed for those with little to no experience with IRT.
Faculty:
Bryce Reeve, PhD Bryce Reeve, PhD, Associate Professor, Lineberger Comprehensive Cancer Center & Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
Chapel Hill, NC, 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 that focus on measuring key health-related quality of life domains in different disease populations will be used throughout the presentation.  The NIH Patient-Reported Outcomes Measurement Information System (PROMIS) project will also be discussed for its relevance for assessing patient-reported outcomes using modern psychometric methods.

8:00 AM – 12:00 PM

Room: Bayside C (4th Floor)
Use of Instrumental Variables in Observational Studies of Treatment Effects
Track: Observational Data Methods
Level: Intermediate. This course is suitable for those with some knowledge of econometrics.
Prerequisite:

Previous attendance at the short course “Introduction to Database Analysis of Observational Studies of Treatment Effects” – (or equivalent knowledge) – is recommended.

Faculty:
Benjamin Craig, PhD Benjamin Craig, PhD, Assistant Member, Health Outcomes and Behavior, Moffitt Cancer Center & Associate Professor, Department of Economics, University of South Florida, Tampa, FL, USA
Antoine El Khoury, PhD, MS Antoine El Khoury, PhD, MS, Director, Market Access and Health Economics, Johnson and Johnson, Horsham, PA, USA and Adjunct Assistant Professor, Division of Pharmaceutical Evaluation and Policy, University of Arkansas for Medical Sciences College of Pharmacy, Little Rock, AR, USA
Bradley C. Martin, PhD, RPh, PharmD Bradley C. Martin, PhD, RPh, PharmD, Professor and Head, Division of Pharmaceutical Evaluation and Policy, University of Arkansas for Medical Sciences College of Pharmacy, Little Rock, AR, USA
Course Description:

In any non-randomized study, selection bias is a potential threat to the validity of conclusions reached. Failure to account for sample selection bias can lead to conclusions about treatment effectiveness or treatment cost that are not really due to the treatment at all, but rather to the unobserved factors that are correlated with both treatment and outcomes. Sample selection models provide a test for the presence of selection bias. These models also provide a correction for selection bias, enabling an investigator to obtain unbiased estimates of treatment effects. This course will discuss the various models and their applications, and in particular will address instrument variables (two-stage least squares, intuition, RCTs), including an overview of examples from the current literature. Participants will benefit from interactive exercises using instrumental variables and sample selection techniques using STATA.

For those who have STATA loaded on their laptops, you are encouraged to bring your laptop.
SUNDAY, MAY 19, 2013 - AFTERNOON COURSES: 1:00 PM – 5:00 PM
1:00PM – 5:00PM
Room: Bayside B (4th Floor)
The Reader Expectation Approach to Professional Writing
Track: Use of Pharmacoeconomics / Economic / Outcomes Research Information
Level: Introductory.
Faculty:
George D. Gopen, JD, PhD, Professor, Practice of Rhetoric, Duke University, Durham, NC, USA
Course Description: This course approaches written communication from the perspective of the reader. In the professional world, no one cares how hard the writer tried nor how much they have improved since last time. To understand the language better we should get to know as fully as possible how readers actually go about the act of interpretation. This short course will introduce participants to five essential components of professional writing, the first steps towards gaining new and better control of written communication.

1:00 PM – 5:00 PM

Room: Bayside C (4th Floor)
Discrete Event Simulation for Economic Analyses – Applications
Track: Modeling Methods
Level: Intermediate. This course is designed for those with some understanding of discrete event simulation (equivalent to attendance at the short course “Discrete Event Simulation for Economic Analysis – Concepts”) and who wish to have more practical modeling experience.
Faculty:
Jaime Caro J. Jaime Caro, MDCM, FRCPC, FACP, Adjunct Professor of Medicine, Adjunct Professor of Epidemiology and Biostatistics, McGill University, Montreal PQ, Canada
Jörgen  Möller

Jörgen Möller, MSc Mech Eng,Vice-President, Modeling, United BioSource Corporation, Hammersmith, UK and Associate Researcher, Division of Health Economics, Faculty of Medicine, Lund University, Sweden

Denis  Getsios

Denis Getsios, Senior Director, Senior Research Scientist, United BioSource Corporation
Lexington, MA, USA

Course Description:

This course is structured around practical discrete event simulation exercises. Topics to be covered are: components of a DES; how do you build a model; modeling of processes and resource use; modeling of variables and decisions. Simple animation will be demonstrated. We will use ARENA to build entry level models. Instructions for downloading training version of Arena will be distributed prior to the course. 

Participants who wish to have hands-on experience must bring their laptops with ARENA installed.

1:00 PM – 5:00 PM

Room: Grand Ballroom E (5th Floor)
Introduction to Outcomes Research for Medical Devices and Diagnostics
Track: Outcomes Research Methods
Level: Introductory. This introductory course is designed for those with little or no experience with outcomes research for medical device and diagnostic technologies.
Faculty:
Seema Sonnad

Seema Sonnad, PhD, Director of Health Services Research, The Value Institute, Christiana Care Health System, Newark, DE

Stacey Ackerman Stacey Ackerman, MSE, PhD, Vice President, Covance Market Access Services, San Diego, CA, USA
Course Description:

This introductory course will present outcomes research practices that are specifically tailored for the fast-paced medical device and diagnostics technology environment and address issues specific to research methods most suitable to devices and diagnostic technologies. The course will cover conducting research on clinical outcomes, economic outcomes, and patient-reported outcomes as described in the ISPOR publication, Therapeutic & Diagnostic Device Outcomes Research (Lawrenceville, NJ: ISPOR, 2011). Outcomes research for medical devices and diagnostics will be differentiated from research primarily intended to assess drug-related outcomes. The evidence hierarchy for medical devices and diagnostic procedures including how outcomes research results are used in coverage and reimbursement decisions will be discussed.  Course materials will primarily focus on the US regulatory system. 


1:00 PM – 5:00 PM

Room: Nottoway (4th Floor)
Network Meta-analysis for Indirect Treatment Comparison
Track: Outcomes Research Methods
Level: Intermediate. This course is of an intermediate level, requiring at least a basic knowledge of meta-analysis and statistics.
Prerequisite: The short course, “Meta-Analysis & Systematic Review for Comparative Effectiveness Research” is a prerequisite for this course. Participants must have knowledge of statistical methods.
Faculty:
Joseph Cappelleri, PhD, MPH Joseph Cappelleri, PhD, MPH, Senior Director, Pfizer Inc., New London, CT, USA
Jeroen Jansen, PhD Jeroen Jansen, PhD, Vice President, Health Economics & Outcomes Research, MAPI Consultancy, Boston, MA, USA
Course Description: For several medical questions of interest, many treatment options exist for the same indication. These treatments may have been compared against placebo or against each other in clinical trials. Knowing whether one specific treatment is better than placebo or some other specific comparator is only a fragment of the big picture, which should incorporate all available information. Ideally, one would like to know how all the different treatment options rank against each other and how big the differences are in effect size between all the available options. Network meta-analysis offers a quantitative method of integrating all the data from all the available comparisons. Based in part on two ISPOR Task Force Reports on Indirect Treatment Comparisons, the fundamentals and concepts of network meta-analysis will be presented, which is especially useful when there’s little or no evidence from direct comparisons. Network meta-analysis provides an integrated and unified analysis that incorporates all direct and indirect comparative evidence about treatments. Nevertheless, the evaluation of networks also presents special challenges and caveats, which will also be highlighted in this course. The material in this course is motivated by instructive and real examples. Case studies are implemented with the WinBUGS package.

1:00 PM – 5:00 PM

Room: Oak Alley (4th Floor)
Use of Propensity Scores in Observational Studies of Treatment Effects
Track: Observational Data Methods
Level: Intermediate. This course is designed for those with little experience with this methodology but some knowledge of observational databases.
Prerequisite: Previous attendance at the short course “Introduction to Database Analysis of Observational Studies of Treatment Effects” – or equivalent knowledge – is recommended.
Faculty:
John Seeger, PharmD, DrPH John Seeger, PharmD, DrPH, Lecturer in Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Harvard Medical School/Brigham and Women's Hospital, Boston, MA, USA
Course Description: In 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. 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.

1:00 PM – 5:00 PM

Room: Grand Ballroom D (5th Floor)
Applying Mixed Methods to Establish Content Validity of Patient-Reported Outcome, Clinician-Reported Outcome and Observer-Reported Outcome Assessment Instruments
Track: Patient Reported Outcomes Methods
Level: Advanced. This course assumes attendees have a basic understanding of qualitative interviewing methods to support a conclusion of content validity of COA instruments and general principles of evaluating measurement properties of outcome instruments.
Faculty:
Jeremy Hobart, PhD, FRCP

Jeremy Hobart, PhD, FRCP, Consultant Neurologist, Plymouth Hospitals NHS Trust and Professor of Clinical Neurology and Health Measurement, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK

Stefan J. Cano, PhD

Stefan J. Cano, PhD, Associate Professor, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK

Donald Patrick, MSPH, PhD Donald Patrick, MSPH, PhD, Professor, University of Washington, Seattle Quality of Life, Seattle, WA, USA
Laurie Burke Laurie Burke, RPh, MPH, Director, SEALD, CDER/FDA , Silver Spring, MD, USA
Course Description:

This course focuses on the use of mixed methods - qualitative and quantitative - to establish the content validity of patient-reported outcome, clinician-reported outcome and observer-reported outcome assessments, which are intended for use in clinical trials to provide evidence to support medical product labeling claims in the US and Europe. In this course we will address: a) how to generate a hypothesized conceptual framework of an instrument that takes potential claims into consideration and helps to identify the goal of measurement by linking concepts with anticipated scores; b) the qualitative research foundation for eliciting potential items that can be arrayed along the continuum of item response categories, including methods of item elicitation and cognitive interviewing; c) the rationale and principles underpinning quantitative analysis to support the content validity of the proposed item content; d) similarities and difference between traditional and modern psychometric methods for this purpose; and e) the key quantitative analyses that can be utilized in a mixed methods approach to establishing content validity. Rasch Measurement Theory will be used as the basis for quantitative analyses. After this course, participants will have a working knowledge of the role of supplementary quantitative evidence, as part of a mixed methods approach to establish and document content validity of a COA instrument.  Examples will be given throughout on the efficient application of a mixed method approach to instrument development, evaluation, and modification, with specific focus on the central issues surrounding content validity. This course will also provide a hands-on instruction in using the Rasch Unidimensional Measurement Model software package (RUMM2030) in order to analyze data for internal construct validity, reliability, category probability curves, differential item functioning, and scaling characteristics.


1:00 PM – 5:00 PM

Room: Bayside A (4th Floor)
Agent-Based Modeling (ABM) for Economic Evaluations
Track: Modeling Methods
Level: Intermediate.
Faculty:
Jagpreet Chhatwal, PhD

Jagpreet Chhatwal, PhD, Assistant Professor, Health Policy & Management, Clinical & Translational Science and Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Oguzhan Alagoz, PhD Oguzhan Alagoz, PhD, Associate Professor of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
Course Description: The ISPOR-SMDM Modeling Good Research Practices Task Force recommends the use of the agent-based modeling approach when stochastic effects, complex interactions between behavior and disease, or distinctly non-random mixing patterns are important. Agent-based models (ABMs) are capable of analyzing complex system dynamics by measuring the direct and indirect effects that may arise from communicable disease control interventions. This course is designed to train researchers and practitioners to build dynamic agent-based models for economic evaluations of drugs and vaccines. It will cover the basics of ABMs, areas of applications, and hands-on tutorials of the development of ABM simulations using NetLogo. NetLogo is a freely available and easy to implement modeling platform, and has extensive libraries and documentation to aid model building. The focus of the applications will be on pharmacoeconomic problems. Participants who wish to have hands-on experience should bring their personal laptops with NetLogo installed. 

1:00 PM – 5:00 PM

Room: Grand Chenier (5th Floor)
Value of Information and Probabilistic Analysis
Track: Modeling Methods
Level: Advanced. This course is for those with experience in decision modelling. Participants should be familiar with Markov models and have experience working in Excel.
Faculty:
Susan Griffin, PhD

Susan Griffin, PhD, Senior Research Fellow, Centre for Health Economics, University of York, Heslington, York, UK

Marta Soares, MSc

Marta Soares, MSc, Research Fellow, Centre for Health Economics, University of York, Heslington, York, UK

Course Description: This course will focus on how to analytically explore decision uncertainty and its consequences. Faculty will explain the analytic methods that underlie explorations of decision uncertainty, including cost-effectiveness acceptability curves, cost-effectiveness planes, and value of information analysis. We will start off by describing multi- or uni-variate sensitivity analyses and its drawbacks. The main focus will be on implementing probabilistic sensitivity analyses and assessing the consequences of uncertainty using value of further research analyses. There will be a practical component to this course, where the methods outlined will be put in practice. Also, practical applications from the literature will be shown. Students who wish to have hands-on experience must bring their laptops with Microsoft Excel installed and the capability to access and run macros written in Visual Basic.

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