Pre-Meeting Short Courses - Full Day

Sunday, October 18, 2009: 9:00 AM
Renaissance Hollywood Hotel

* Candidate for the Lee B. Lusted Student Prize Competition

9:00 AM
Job Kievit, MD, PhD, Leiden University Medical Center (LUMC), Leiden, Netherlands
Course size limit: 25 Level: Beginner Background: This course is intended for individuals new to decision analysis who wish to learn the basic principles of formulating and analyzing clinical decisions. The course is "hands-on" and uses in-class exercises to teach the building blocks of decision analysis. These blocks are Bayes' rule, interpreting the results of diagnostic tests, formulating a medical decision problem, measuring utilities and risk attitude, calculating expected utility, and performing sensitivity analysis, as well as (at an elementary level) cost-effectiveness analysis. Objectives and Course Description: First participants are made familiar with the methods of MDM through teaching and exercises. Then the course o brings these methods home by applying them to participants' clinical or research-questions o in addition, in this way aims to show what MDM may or may not do, and thus interactively demonstrate its strengths and limitations at the level of the individual, of patient groups and of society To this end course participants are invited to send in their own clinical or research decision questions by email before the course,(a selection of) which will be used for teaching purposes. Format, Requirements, Target Audience: Participants will be asked to send in a short form with their own decision problem(s) by email.
9:15 AM
Alan Schwartz, PhD, University of Illinois at Chicago, Chicago, IL, Olga Kostopoulou, PhD, MSc, BA, King's College London, London, United Kingdom and Robert M. Hamm, PhD, University of Oklahoma Health Sciences Center, Oklahoma City, OK
** This course will be taught in two half-day modules which can be taken as a full day course or as two half day courses. Course size limit: none Level: Beginner Background: This course introduces participants to descriptive findings and psychological theory related to making decisions in health and medicine. Knowledge of the psychology of medical decision making can be used to improve explanations and predictions of patient and physician behavior, and to design behavioral interventions. Objectives and Course Description:
  • To understand the psychological processes involved in medical decision making by patients and physicians, in the contexts of probability/diagnosis/risk and preference/choice.
  • To describe research methods used for studying the psychology of medical decision making.
  • To understand patient and physician vulnerability to cognition based errors.
  • To develop approaches to support decision makers based on psychological theory.
Concepts, skills, or experiences that the participants will acquire by attending the course. The characteristics of the cognitive system: large memory, limited attention span, pattern recognition ability. Differences between analytical and intuitive processing and their implications for judgment and decision making The judgments needed for decision making, and the strategies people use to draw on their knowledge in making judgments. Types of errors physicians and patients may make when thinking about decisions. Strategies for minimizing the impact of errors and for supporting accurate thinking about decisions. Format, Requirements, Target Audience: This course will be taught in two half-day modules which can be taken as a full day course or as two half day courses. The first module will be offered in the AM will focus on the psychology of probability, including diagnosis and risk perception. The second module will be offered in the PM will focus on the psychology of preference and choice, including valuation and descriptive models of decision making. The course involves brief lectures, demonstrations, and small and large group discussions. Attendees should expect to be actively involved in discussions of psychological phenomena as they relate to their clinical, teaching, or research interests.
9:30 AM
Ingram Olkin, PhD, Stanford University, Palo Alto, CA and Thomas A. Trikalinos, MD, PhD, Tufts Medical Center, Boston, MA
Course size limit: none Level: Beginner Background: Meta-analysis is a formal, systematic method to synthesize the results of independent studies, considering and integrating the combined weight of evidence to determine the effect of an intervention. Meta-analysis is being used increasingly in the medical and health sciences to inform and guide practice and policy, in areas as disparate as estimating the effectiveness of mammography in detection of breast cancer and the consistency of gene-disease association studies. A Google Scholar search on meta-analysis identified 589,000 hits in medicine, 293,000 in health policy, and 102,000 in genetics. The information explosion in almost every field coupled with the movement towards evidence-based decision making and cost-effective analysis has catalyzed development of more rigorous procedures to synthesize the results of independent studies. Objectives and Course Description: This workshop will provide an historical perspective of meta-analysis, and discuss methodological issues such as various types of bias and heterogeneity on the conduct and interpretation of meta-analyses. There will be extensive discussion of the appropriateness and use of statistical methods for combining data across studies, including nonparametric and parametric models; effect sizes for proportions, fixed versus random effects, regression and ANOVA models; multivariate models for proportions and standardized mean differences, treatment of zero cells, models with missing data, and special methods and issues in genetic applications.
  • Understand the potential value of and theory underlying the conduct of meta-analysis of independent studies
  • Understand conditions under which meta-analyses can be performed and common factors that limit or confound the metaanalysis conduct and interpretation.
  • Learn and understand a range of statistical methods for analyzing and interpreting meta-analysis studies
Format, Requirements, Target Audience: Didactic lectures and interactive discussion of theory, potential confounders and limitations, and statistical methods, using case study examples from published medical literature.
9:45 AM
Jeffrey Hoch, PhD, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada, Katia Noyes, PhD, University of Rochester, Rochester, NY, Ahmed M. Bayoumi, MD, MSc, Centre for Research on Inner City Health, the Keenan Research Centre in the Li Ka Shing Knowledge Institute, Toronto, ON ON ON, Canada Canada Canada and Elisabeth A.L. Fenwick, PhD, University of Glasgow, Glasgow, United Kingdom
Course size limit: none Level: Intermediate Background: Many funding agencies and academic publications now require cost-effectiveness estimates and measures of uncertainty around those estimates. This course will introduce the concepts behind probabilistic sensitivity analysis and net benefit regression and provide participants with practical skills to produce numeric and graphical outputs that reflect estimates and their uncertainty. This course is directed towards health outcomes researchers in academia, industry, government or regulatory bodies without special training in health economics. Participants will be expected to know how to perform simple regeression analysis. Objectives and Course Description: This course will provide participants with the conceptual background to understand why it is important to produce cost-effectiveness estimates and measure their uncertainty and will present some commonly used methods to do this. Specific objectives are:
  1. To understand the conceptual basis for measuring uncertainty in costs, effects, and cost-effectiveness estimates.
  2. To understand the relationship between the scatter plot of incremental costs and incremental effects and how these relate to confidence ellipses and cost-effectiveness acceptability curves
  3. To be able to build confidence ellipses and cost-effectiveness acceptability curves using Excel or Stata
  4. To understand how regression analysis can be used to estimate cost-effectiveness in person-level data using the net benefit approach.
  5. To understand how regression analysis can be used to characterize uncertainty in person-level data using the net benefit approach.
  6. To understand the strengths and weaknesses of alternative methods for presenting uncertainty in cost-effectiveness analyses.
Format, Requirements, Target Audience: The course will consist of didactic learning (theory bursts) followed by interactive tutorials ("hands on" exercise). Participants are required to bring their own laptops loaded with either Excel or Stata. Using guidance and data files provided by the instructors, each participant will learn to generate cost-effectiveness acceptability curves (CEACs) and confidence ellipses in Excel and Stata.
10:00 AM
Roy M. Poses, MD, Brown University, Providence, RI and Wally Smith, MD, Virginia Commonwealth University, Richmond, VA
Course size limit: none Level: Intermediate Background: Evidence-based medicine (EBM) integrates the best scientific evidence with clinical expertise and patient values. However, physicians often fail to practice in accord with EBM principles and many attempts to change physician behavior to make it more evidence based have failed. Objectives and Course Description: This course will examine reasons physician practice at times fails to adhere with EBM principles and explore promising interventions to improve EBM-based practice. The impact of human thinking strategies designed to cope with inherent cognitive limitations that may lead to judgments and decisions that fail to conform with normative ideals (with emphasis on judgment and decision biases and heuristics) will be discussed for each stage of the evidence-based decision making process: identifying options and their outcomes; assessing probability of outcomes; assessing value of options; and combining information to make a decision. The course will examine the impact of organizations and culture on medical decision making and practice, which increasingly is provided from within large organizations whose leadership, structures, processes, incentives and environments (e.g., time and economic pressure; conflicts-of-interest) may undermine EBM-based care. The growing presence and impact of stealth marketing, special politically correct pleadings, suppression and manipulation of research, perverse bureaucratic and financial incentives, and intimidation and coercion that may challenge and undermine elements of an EBM approach will be identified, described and discussed. The course will conclude with review and exploration of promising approaches based on findings in the cognitive psychology to address physicians' human cognitive limitations that may help physicians practice more in accord with EBM, as well as general approaches to defend evidence-based decision practice from health care environment threats.
  • Understand principles and impact of human cognitive behavior used to cope with cognitive limitations at each stage of the clinical decision making process.
  • Understand organizational challenges to EBM practice.
  • Understand environmental and social/cultural threats to increasing EBM practice.
  • Understand promising approaches for facilitating physician EBM practice.
Format, Requirements, Target Audience: Didactic review of the cognitive psychology and organizational theory literature and interactive critical review and discussion of case studies and interventions to improve evidence-based practice.