Pharmacoeconomic / Economic Methods

Pharmacoeconomics for Decision-Makers
Room: Coral Ballroom B

Faculty: Lorne Basskin PharmD, Director, Pharmacy Services, Healthsouth Sunrise Rehab Hospital, Cooper City, FL, USA

Course Description: This course is designed to teach clinicians and new researchers how to incorporate pharmacoeconomics into study design and data analysis. Participants will learn how to collect and calculate the costs of different alternatives, determine the economic impact of clinical outcomes, and how to identify, track and assign costs to different types of health care resources used. The development of economic protocols and data collection sheets will be discussed. Different pharmacoeconomic models and techniques will be demonstrated and practiced in lectures and case studies. These include cost-minimization, cost-of-illness, cost-effectiveness, cost-benefit, and cost-utility analysis. Decision analysis, sensitivity analysis, and discounting, will all be demonstrated and practiced. Participants will also learn to compare and evaluate interventions such as drugs, devices and clinical services. This course is suitable for those with little or no experience with pharmacoeconomics.

Real World Data Methods

Retrospective Database Analysis
Room: Coral Ballroom C

Faculty: William H. Crown PhD, President, i3 Innovus, Waltham, MA, USA; Onur Baser MS, PhD, President, STATinMED Research, Director of Analytic Group, M-SCORE, and Assistant Professor of Surgery, Department of Surgery, University of Michigan, Ann Arbor,
MI, USA

Course Description: Large administrative claims databases provide a unique opportunity to examine retrospectively the effects of drug use on clinical and economic outcomes in "real world" settings. This course will cover a discussion of the "ISPOR Checklist for Retrospective Database Studies - Report of the ISPOR Task Force on Retrospective Databases" - Report of the ISPOR Task Force on Retrospective Databases and selected topics related to estimators and sampling distributions, properties of sampling distributions (unbiasedness, efficiency, mean square error), and ordinary least squares (OLS) regression. OLS model assumptions and the implications of violations (e.g., heteroscedasticity, multicollinearity, autocorrelation) will also be discussed. More complex topics beginning with the problem of endogeneity, identification, instrumental variables, sample selection models, and propensity score models, maximum likelihood methods and the estimation of limited dependent variables models including logit, multinomial logit, count models, and survival models will be discussed. This course will assume participants have knowledge of statistical methods through OLS regression and experience in the analysis of administrative claims databases.

Modeling Methods

Bayesian Analysis: Overview and Applications
Room: Palani Sailfish - 2nd Floor

Faculty: 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; Christopher S. Hollenbeak PhD, Associate Professor, Surgery and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA

Course Description: The first portion of this course is designed to provide an overview of the Bayesian approach and its applications to health economics and outcomes research. The course will cover basic elements of Bayesian statistics, contrasting briefly with classical (frequentist) statistics and introduce available statistical packages. The second portion of this course will focus on the Bayesian “informative prior.” Several example vignettes of how a Bayesian analysis can be used within outcomes modeling problems will be presented. Participants will learn how a Bayesian approach is different and why it is useful for their work and what tools are available to them. Participants of this course should be prepared to use their own laptops as the exercises presented use interactive software. This course is designed for those with a limited understanding of Bayesian statistical concepts.

SATURDAY, MAY 16, 2009 (Morning Courses)
8:00 AM - 12:00 PM

Use of Pharmacoeconomics / Economic / Outcomes Research Information

Elements of Pharmaceutical/Biotech Pricing I – Introduction
Room: Anemone - 2nd Floor

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

Course Description: This course will give participants a basic understanding of the key terminology and issues involved in pharmaceutical pricing decisions. It will cover the tools to build and document product value including issues, information and processes employed (including pricing research); the role of pharmacoeconomics and the differences in payment systems that help to shape pricing decisions. These tools will be further explored through a series of interactive exercises. This course is designed for those with limited experience in the area of pharmaceutical pricing and will cover topics within a global context.

Pharmacoeconomic / Economic Methods

Cost-Effectiveness Analysis alongside Clinical Trials
Room: Yellowtail Room

Faculty: Scott Ramsey MD, PhD, Full Member and Professor, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Richard Willke PhD, Senior Director, Global Outcomes Research, Pfizer Inc., Peapack, NJ, USA

Course Description: The growing number of prospective clinical/economic trials reflects both widespread interest in economic information for new technologies and the regulatory and reimbursement requirements of many countries that now consider evidence of economic value along with clinical efficacy. This course will present the design, conduct, and reporting of cost-effectiveness analyses alongside clinical trials based on, in part, the "Good Research Practices for Cost-Effectiveness Analysis alongside Clinical Trials: The ISPOR RCT-CEA Task Force Report." Trial design, selecting data elements, database design and management, analysis, and reporting of results will be presented. Trials designed to evaluate effectiveness (rather than efficacy), as well as clinical outcome measures will be discussed. How to obtain health resource use and health state utilities directly from study subjects and economic data collection fully integrated into the study will also be discussed. Analyses guided by an analysis plan and hypotheses, an incremental analysis using an intention to treat approach, characterization of uncertainty, and standards for reporting results will be presented. This course is an introductory/intermediate level. Familiarity with economic evaluations will be helpful.

Modeling Methods

Introduction to Decision Analysis
Room: Coral Ballroom A

Faculty: Mark S. Roberts MD, Professor of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Course Description: Decision analysis is a tool that uses an explicit, quantitative structure to describe and analyze complex health care decisions. This course will provide an introduction to the principles and practice of decision analysis. Upon completion of the course, participants will be able to evaluate the appropriateness of decision analysis in different settings, construct simple decision trees, understand the basic mechanics of tree evaluation and sensitivity analysis, and acquire skill in the interpretation of a published decision analysis. Extension of basic techniques, such as cost-effectiveness analysis and the assessment of patient preferences will be briefly discussed. Class exercises will be used to illustrate these principles. This course is suitable for those with little experience with decision analysis.

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

Strategy and Guidelines for Conducting and Analyzing Patient-Reported Outcomes Research in Clinical Trials
Room: Damselfish - 2nd Floor

Faculty: Bruce Crawford MPH, MA, General Manager, Asia, Mapi Values, Tokyo, Japan; Kathleen Rosa MS, PhD, Director, Psychometrics and Statistics, Mapi Values, Boston, MA, USA; Jeffrey McDonald MS, Statistician, Mapi Values Boston, MA, USA

Course Description: This course will discuss critical issues related to conducting patient-reported outcomes (PRO) in clinical trials, with a focus on regulatory issues around supporting claims.  Topics covered will include scheduling the administration according to specific demands of the indication/treatment, methods for analysis, handling missing data and adjusting for multiplicity. This is an introductory level course.

SATURDAY, MAY 16, 2009 (Afternoon Courses)
1:00 PM - 5:00 PM

Pharmacoeconomic / Economic Methods

Financial Impact / Cost of Illness
Room: Fantail - 2nd Floor

Faculty: Josephine Mauskopf PhD, Vice President, Health Economics, RTI Health Solutions, Research Triangle Park, NC, USA; C. Daniel Mullins PhD, Professor and Chair, Pharmaceutical Health Services Research, University of Maryland School of Pharmacy,
Baltimore, MD, USA

Course Description: This course will describe methods to determine the costs associated with a health condition and the budget impact of new technologies for that condition. The course will present incidence- and prevalence-based costing strategies. Treatment algorithms and event-based approaches will be demonstrated for disease-specific costs from different decision-maker perspectives. Both static and dynamic methods for estimating the budget impact of adding a new drug to a health plan formulary will be presented. Issues related to imputing missing data will also be discussed. This course is designed for those with some experience with pharmacoeconomic analysis.

Finding and Extracting Cost Data
Room: Coral Ballroom A

Faculty:  L. Clark Paramore MSPH, Executive Director and Research Scientist, Center for Health Economics & Science Policy, United BioSource Corporation, Lexington, MA, USA; Steve Blume MS, Research Scientist, Center for Health Economics & Science Policy, United BioSource Corporation, Bethesda, MD, USA

Course Description: This course will focus on practical aspects of cost development for pharmacoeconomic studies. The objective is to help the participant bridge the gap between understanding pharmacoeconomic theory and the practice of developing cost estimates. Factors to consider when costing pharmacoeconomic analyses, such as perspective, data sources, data classification systems, developing resource use profiles, obtaining unit costs, and making cost adjustments will be presented. Examples of issues encountered when identifying and extracting cost data will be discussed. This course is designed for those with some experience with pharmacoeconomic analysis.

Modeling Methods

Modeling: Design and Structure of a Model
Room: Yellowtail Room

Faculty: Marc Botteman MA, Managing Partner and Director of Health Economics, PharMerit North America LLC, Bethesda, MD, USA; Ben van Hout PhD, Scientific Director, PharMerit, Rotterdam, The Netherlands & Professor in Medical Technology Assessment, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands

Course Description: This course will include a review of modeling techniques (Markov models, discrete event simulations, and Monte Carlo techniques) including a discussion of the "ISPOR Principles of Good Practice for Decision Analytic Modeling in Health Care Evaluations." Markov models and first and second order Monte Carlo simulations including data identification, data modeling, and data incorporation will be demonstrated. Using a series of examples, the course will carefully review the practical steps involved in developing and using these kinds of models. Examples will be presented using Microsoft Excel, supplemented with add on simulation software. This course will cover the practical steps involved in the selection of models and options in modeling of data inputs. Participants should have a basic understanding of decision analysis. Familiarity with the course "Introduction to Decision Analysis" (or equivalent knowledge) is recommended.

Advanced Patient-Reported Outcomes Assessment: Psychometric Methods
Room: Anemone - 2nd Floor

Faculty:  Bruce Crawford MPH, MA, General Manager, Asia, Mapi Values, Tokyo, Japan; Kathleen Rosa MS, PhD, Director, Psychometrics and Statistics, Mapi Values, Boston, MA, USA; Jeffrey McDonald MS, Statistician, Mapi Values Boston, MA, USA

Course Description: This course will discuss psychometric analysis and the application of various techniques such as structural equation modeling (SEM), factor analysis (FA) and item response theory (IRT) in testing patient-reported outcomes (PRO) instruments, measures and construct / criterion validity.  Validity indicates how well a measurement tool allows us to infer something about the true nature and value of the object or system being considered.  Instructors will explain and demonstrate how to analyze observed and latent constructs and variables within a model as well as to test the validity of a PRO measure.  Specific examples will be given to highlight how researchers can apply these techniques to test methods, criteria and new measures.  This is an advanced course designed for those with a working knowledge of QoL/PRO methods.

Real World Data Methods

Instrumental Variables in Addressing Selection Bias in Observational Studies
Room: Damselfish - 2nd Floor

Faculty: Benjamin M. Craig PhD, Assistant Member, Health Outcomes & Behavior, Moffitt Cancer Center & Courtesy Associate Professor, Department of Economics, University of South Florida, Tampa, FL, USA; Antoine C. El Khoury PhD, MS, Manager, Outcomes Research, Merck & Co Inc., West Point, PA, USA, & Adjunct Assistant Professor, Division of Pharmaceutical Evaluation and Policy, University of Arkansas for Medical Sciences College of Pharmacy, Little Rock, AR, USA; Bradley Martin PhD, RPh, PharmD, Professor and Division Chair, College of Pharmacy, University of Arkansas for Medical Sciences, Department of Pharmacy Practice, 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).  This course is suitable for those with some knowledge of econometrics.