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Summer 2008 Program on Meta-analysis: Synthesis and Appraisal of Multiple Sources of Empirical Evidence
June 2-13, 2008
Scientific Context
Three Challenges
Description of Activities
Application
Further Information
Scientific Context
Seldom is there only a single empirical research study relevant to a question of scientific
interest. However, both experimental and observational studies have traditionally been analyzed in
isolation, without regard for previous similar or other closely related studies. A new research
area has arisen to address the location, appraisal, reconstruction, quantification, contrast and
possible combination of similar sources of evidence. Variously called meta-analysis, systematic
reviewing, research synthesis or evidence synthesis, this new field is gaining popularity in
diverse fields including medicine, psychology, epidemiology, education, genetics, ecology and
criminology. Statistical methods for combining results across independent studies have long existed,
but require renewed consideration, development and wider dissemination by inclusion in the
mainstream statistics curriculum. The possibility that the due consideration of all relevant
evidence should be accepted as standard practice in statistical analyses deserves investigation.
The combination of results from similar studies is often known simply as 'meta-analysis'. Common
examples are combining results of randomized controlled trials of the same intervention in
evidence-based medicine; of correlation coefficients for a pair of constructs measured similarly
across studies in social science; or of odds ratios measuring association between an exposure and
an outcome in epidemiology. More complex syntheses of multiple sources of evidence have developed
recently, including combined analyses of clinical trials of different interventions, and combined
analysis of data from multiple microarray experiments (sometimes called cross study analysis). For
straightforward meta-analyses, general least-squares methods may be used, but for complex
meta-analyses, the technical statistical approach is not so obvious. Often likelihood and Bayesian
approaches provide very different perspectives; and in practice the possible benefits of more
complex approaches may be hard to discern as many meta-analyses are compromised by limited or
biased availability of data from studies as well as by varying methodological limitations of the
studies themselves.
The presence of multiple sources of evidence has long been a recognized challenge in the
development and appraisal of statistical methods — from Laplace and Gauss to Fisher and Lindley.
In the 1980s Richard Peto argued that a combined analysis would be more important than the
individual analyses, a view taken still further by Greenland who has suggested that that
individual study publications should not attempt to draw conclusions at all, but should instead
only describe and report results, so that a later meta-analysis can more appropriately assess the
study's evidence fully informed by other study designs and results. Will combined analyses
actually replace individual analyses (or at least decrease their impact)? If so, it is time to
re-examine the perennial problems of statistical inference in this context.
Three Challenges
- To substantiate and clarify how existing statistical methodology can effectively combine multiple sources of evidence, given perfect conduct and reporting of all studies.
- To identify statistical areas in need of development or improvement, both in theory and application, for the practical situations of studies having methodological limitations and studies providing biased or incomplete data.
- To identify and develop material and pedagogy for undergraduate and graduate programs in statistics, to allow future statisticians to deal effectively with multiple sources of evidence, and to motivate further development of new methodology.
This program comprises two weeks of research, mixing tutorials, research presentations and working
group activities on the subject. The goal of this program is three-fold: 1) to bring the area to
the attention of statistical researchers, whose expertise is critical to substantiate and clarify
the necessary statistical theory and methodology; 2) to nurture the necessary interdisciplinary
collaboration and communication between statistical researchers and statisticians who currently
work or plan to work with basic and applied science researchers and 3) to provide an entry point
into the field to interested students and faculty, and to allow researchers already specialized in
the domain to exchange recent results and information.
Program Leaders: Keith O'Rourke (Duke University), Joseph Beyene (University of Toronto), Vanja Dukic (University of Chicago),
Julian Higgins (UK Medical Research Council, Cambridge), Peter Hoff (University of Washington),
Ken Rice (University of Washington) and Dalene Stangl (Duke University).
Description of Activities
Workshop
The meeting consists of five days of conference culminating in the formation of Working Groups,
followed by a week of Working Group meetings and research.
7:45-8:45 |
Registration and Continental Breakfast |
8:45-9:00 |
Welcome |
9:00-10:15 |
Overview
Meta-analysis: Statistical Methods for Combining the Results of Independent Studies
Ingram Olkin, Stanford University |
10:15-10:45 |
Coffee Break |
10:45-12:00 |
Overview Continued |
12:00-1:00 |
Lunch |
1:00-3:00 |
Overview Continued |
3:00-3:30 |
Coffee Break |
3:30-5:00 |
Likelihood Basis for Multiple Data Sources
Keith O'Rourke, Duke University |
7:45-9:00 |
Registration and Continental Breakfast |
9:00-10:15 |
"Sparse Evidence"
Vanja Dukic, University of Chicago
Ken Rice, University of Washington
Keith O'Rourke, Duke University |
10:15-10:45 |
Coffee Break |
10:45-12:00 |
Continued |
12:00-1:00 |
Lunch |
1:00-3:00
| "Common Parameter Focus"
Vanja Dukic, University of Chicago
Ken Rice, University of Washington
Keith O'Rourke, Duke University
TBA |
3:00-3:30 |
Coffee Break |
3:30-5:00 |
"Data Analysis Session"
TBA |
7:45-9:00 |
Registration and Continental Breakfast |
9:00-10:15 |
"Bayes and the Likelihood MA"
Vanja Dukic, University of Chicago
Ken Rice, University of Washington
Keith O'Rourke, Duke University |
10:15-10:45 |
Coffee Break |
10:45-12:00 |
Continued |
12:00-1:00 |
Lunch |
1:00-3:00 |
"Practicalities"
Julian Higgins, Cambridge University |
3:00-3:30 |
Coffee Break |
3:30-5:00 |
"Data Analysis Session"
TBA |
5:00-5:30 |
Poster Advertisement Session: 2 minute ads by each poster presenter |
6:30-8:30 |
Poster Session and Reception
SAMSI will provide poster presentation boards and tape. The board dimensions
are 4 ft. wide by 3 ft. high. They are tri-fold with each side being 1 ft. wide and the center
2 ft. wide. Please make sure your poster fits the board. The boards can accommodate up to 16
pages of paper measuring 8.5 inches by 11 inches. |
Thursday, June 5, 2008
Radisson Hotel RTP
Opening Workshop
7:45-8:45 |
Registration and Continental Breakfast |
8:45-9:00 |
Welcome |
9:00-10:15 |
"MA as Practiced"
Julian Higgins, Cambridge University |
10:15-10:45 |
Coffee Break |
10:45-12:00 |
"MA-Sports Medicine"
Ian Shrier, McGill University |
12:00-1:00 |
Lunch |
1:00-3:15 |
"MA-diagnostic Test Accuracy Studies"
Constantine Gatsonis, Brown University
"MA-Genetic Epidemiology"
Tom Trikolinos, Tufts Universitiy |
3:15-3:30 |
Coffee Break |
3:30-5:00 |
New Researchers
Combining Information from Randomized and Observational Data: A Simulation Study
Eloise Kaizar, Ohio State University
TBA
Elizabeth Stuart, Johns Hopkins University
TBA
Vanja Dukic, University of Chicago |
7:45-9:00 |
Registration and Continental Breakfast |
9:00-10:15 |
TBA
Sally Morton, RTI or Eugene Demidenko, Dartmouth University |
10:15-10:45 |
Coffee Break |
10:45-12:00 |
The Exact Distributions of Test Statistics Resulting from the Random Effects Model for Meta-Analysis
Dan Jackson, Cambridge University |
12:000-1:00 |
Lunch |
1:00-3:15 |
"Model Conflict: Prior v Likelihood"
Susie Bayarri
TBA
Eugene Demidenko, Dartmouth University or David Dunson, NIEHS |
3:15-3:45 |
Coffee Break |
3:45-5:00 |
"Curriculum"
Joseph Beyene, University of Toronoto |
Working Groups
Working Groups will meet daily to address five focus areas:
- Topic 1: Statistical Inference in the Context of Individual vs. Combined Analyses
- Topic 2: Clarifying the Roles and Impacts of Priors, Given Multiple Sources of Sample and Non-Sample Information
- Topic 3: Computational Challenges (and Breaks) for Meta-analysis
- Topic 4: Curricula and Syllabi to Incorporate Meta-analysis into Statistical Education
- Topic 5: Substantive researchers and users of research. Dialogue and Resolution of Application Issues
Application
Interested individuals should apply, using the ON-LINE APPLICATION FORM. This form also includes the
application for financial support. You will be notified as soon as possible after your application
if your participation will be possible; regrettably, limited seating will preclude acceptance of all
applications. New researchers (graduate students, postdocs, and faculty in the early stages of
their careers) and members of underrepresented groups are especially encouraged to apply.
The application/registration deadline is May 23, 2008
In order to ensure your application is correct, we ask that you:
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refresh/reload the application/registration page to
ensure you have all updates
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type in your information (cutting and pasting
will distort the information we receive)
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make any clarifications/corrections, in the
Special Requests section
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click the submit button only once
Please make reservations at the Radisson RTP as soon as possible.
The SAMSI room block for the Radisson is effective until May 19, 2008. After this date, there is
no guarantee a room will be available. If you have a change in plans, individual room reservations
must be cancelled 72 hours prior to arrival. Check-in is at 3:00 PM; check-out is 12:00 noon.
Further Information
For additional information about the program, send an email to [email protected].
Please send a letter describing your interest, along with a vita (if a new researcher), to the
indicated e-mail address.
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