JSM 2005 Online Program

JSM Activity #CE_20C

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

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CE_20C	Mon, 8/8/05, 1:00 PM - 5:00 PM	MCC-L100 J
Meta-analysis: Statistical Methods for Combining the Results of Independent Studies - Continuing Education - Course
ASA
Instructor(s): Ingram Olkin, Stanford University
Meta-analysis enables researchers to synthesize the results of a number of independent studies designed to determine the effect of an experimental protocol such as an intervention, so that the combined weight of evidence can be considered and applied. Increasingly meta-analysis is being used in the health sciences, education and economics to augment traditional methods of narrative research by systematically aggregating and quantifying research literature. Two meta-analytic examples are the effectiveness of mammography in the detection of breast cancer, and an evaluation of gender differences in mathematics education. The information explosion in almost every field coupled with the movement towards evidence based decision making and cost-effective analysis has served as a catalyst for the development of procedures to synthesize the results of independent studies. In this workshop we provide an historical perspective of meta-analysis, discuss, some of the issues such as various types of bias and the effects of heterogeneity. The methodology will include nonparametric and parametric models, and in particular, will focus on regression as a unified approach.