Coffee and light breakfast available

Welcome, introductions, and overview

Welcome, framing of the meeting, and agenda overview

Welcome from the Institute of Medicine (IOM)

Michael McGinnis, IOM

Opening remarks and meeting overview

Joe Selby, Patient-Centered Outcomes Research Institute

Ralph Horwitz, GlaxoSmithKline

Workshop stage setting

   Session format

   Workshop overview and stage setting
Steve Goodman, Stanford University

Q&A and open discussion

   Session questions:

   How do observational studies contribute to building valid evidence to support effective decision making by patients and clinicians? When are their findings useful?
When are they not?

   What are the major challenges (study design, methodological, data collection/management/analysis, cultural, etc.) facing the field in the use of observational study data for decision making? Please include consideration of the following issues: bias, methodological standards, publishing requirements.

   What can workshop participants expect from the following sessions?

Engaging the issue of bias

Moderator: Michael Lauer, National Heart, Lung, and Blood Institute

   Session format

   Introduction to issue
Sebastian Schneeweiss, Harvard University

   Presentations:

   Instrumental variables and their sensitivity to unobserved biases
Dylan Small, University of Pennsylvania

   An empirical approach to measuring and calibrating for error in observational analyses
Patrick Ryan, Johnson & Johnson

   Respondents and panel discussion:

   John Wong, Tufts University

   Joel Greenhouse, Carnegie Mellon University

Q&A and open discussion

   Session questions:

   What are the major bias-related concerns with the use of observational study methods? What are the sources of bias?

   How many of these concerns relate to methods and how many relate to the quality and availability of suitable data? What barriers have these concerns created for the use of the results of observational studies to drive decision making?

   What are the most promising approaches to reduction of bias through the use of statistical methods?
Through study design (e.g., dealing with issues of multiplicity)?

   What are the circumstances under which administrative (claims) data can be used to assess treatment benefits? What data are needed from electronic health records to strengthen the value of administrative data?

   What methods are best used to adjust for the changes in treatment and clinical conditions among patients followed longitudinally?

   What are the implications of these promising approaches for the use of observational study methods moving forward?

Lunch

Participants will be asked to identify, along with the individuals at their table what they think the most critical questions are for patient centered research outcomes in the topics covered by the workshop. These topics will then be circulated to the moderators of the proceeding sessions.

Generalizing randomized controlled trial (RCT) results to broader populations
Moderator: Harold Sox, Dartmouth College

   Session format

   Introduction to issue
Robert Califf, Duke

   Presentations:

   Generalizing the right question
Miguel Hernán, Harvard University

   Using observational studies to determine RCT generalizability
Eloise Kaizar, The Ohio State University

   Respondents and panel discussion:

   William Weintraub, Christiana Medical Center

   Constantine Frangakis, Johns Hopkins University

Q&A and open discussion

   Session questions:

   What are the most cogent methodological and clinical considerations in the use of observational study methods to test the external validity of findings from RCTs?

   How do data collection, management, and analysis approaches affect generalizability?

   What are the generalizability questions of greatest interest? Or, where does the greatest doubt arise (age, concomitant illness, concomitant treatment)? What examples represent well-established differences?

   What statistical methods are needed to generalize RCT results?

   Are the standards for causal inference from OSs different when prior RCTs have been performed? How does statistical methodology vary in this case?

   What are the implications when treatment results for patients not included in the RCT differ from the overall results reported in the original RCT?

   What makes an observed difference in outcomes credible? Finding the effect shown in the RCT in the narrower population? Replication in more than one environment? The confidence interval of the result? The size of the effect in the RCT?

   Can subset analyses in the RCT, even if they are underpowered, be used to support or rebut the OS finding?

Break

Detecting treatment effect heterogeneity
Moderator: Richard Platt, Harvard Pilgrim Health Care Institute

   Session format

   Introduction to issue

David Kent, Tufts University

   Presentations:

   Comparative effectiveness of coronary artery bypass grafting and percutaneous coronary intervention Mark Hlatky, Stanford University

   Identification of effect heterogeneity using instrumental variables
Anirban Basu, University of Washington

   Respondents and panel discussion:

   Mary Charlson, Cornell University

   Mark Cullen, Stanford University

Q&A and open discussion

   Session questions:

   What is the potential for OSs in assessing treatment response heterogeneity and individual patient decision making?

   What clinical and other data can be collected routinely to affect this potential?

   How can longitudinal information on changes in treatment categories and clinical condition be used to assess variations in treatment responses and individual patient decision making?

   What are the statistical methods for time-varying changes in treatment (including cotherapies) and clinical condition?

   What are the best methods to form distinctive patient subgroups in which to examine heterogeneity of the treatment response?

   What data elements are necessary to define these distinctive patient subgroups?

   What are the best methods to assess heterogeneity in multidimensional outcomes?

   How could further implementation of best practices in data collection, management, and analysis affect treatment response heterogeneity?

   What is needed for information about treatment response heterogeneity to be validated and used in practice?

Summary and preview of next day

Reception

Adjourn

Coffee and light breakfast available

Welcome, brief agenda overview, and summary of previous day
Welcome, framing of the meeting, and agenda overview

Predicting individual responses
Moderator: Ralph Horwitz, GlaxoSmithKline

   Session format

   Introduction to issue

Burton Singer, University of Florida

   Presentations:

   Data-driven prediction models
Nicholas Tatonetti, Columbia University

   Individual prediction
Michael Kattan, Cleveland Clinic

   Respondents and panel discussion:

   Peter Bach, Sloan Kettering

   Mitchell Gail, National Cancer Institute

Q&A and open discussion

   Session questions:

   How can patient-level observational data be used to create predictive models of the treatment response in individual patients? What statistical methodologies are needed?

   How can predictive analytic methods be used to study the interactions of treatment with multiple patient characteristics?

   How should the clinical history (longitudinal information) for a given patient be utilized in the creation of rules to predict the response of that patient to one or more candidate treatment regimens?

   What are effective methodologies for producing prediction rules to guide the management of an individual patient on the basis of their comparability to the results of RCTs, OSs, and archived patient records?

   How can we blend predictive models, which can predict impact of treatment choices, and causal modeling, that compare predictions under different treatments?

Conclusions and strategies going forward
Panel members will be charged with highlighting very specific next steps laid out in the course of workshop presentations and discussions or suggesting some of their own.

   Panel:

   Cynthia D. Mulrow, University of Texas

   Jean R. Slutsky, Agency for Healthcare Research and Quality

   Steven N. Goodman, Stanford University

   Session questions:

   What are the major themes and conclusions from the workshop’s presentations and discussions?

   How can these themes be translated into actionable strategies with designated stakeholders?

   What are the critical next steps in terms of advancing analytic methods?

   What are the critical next steps in developing databases that will generate evidence to guide clinical decision making?

   What are critical next steps in disseminating information on new methods to increase their appropriate use?

Summary and next steps

Comments from the Chairs

Joe Selby, Patient-Centered Outcomes Research Institute

Ralph Horwitz, GlaxoSmithKline

Comments and thanks from the IOM

Michael McGinnis, IOM

Adjourn