"The
scandalous failure of scientists to cumulate scientifically," a 2006 talk
by Sir Iain Chalmers, referred to the tendency of scientists to look at
individual studies in isolation, rather than as part of a systematic review of
the "body of evidence" on a given subject. Meta-analysis
provides the statistical methodology to aggregate multiple studies for a
general conclusion. Learn more in Dr. Michael Borenstein's and Dr. Hannah
Rothstein's online course, “Meta Analysis,” at statistics.com. For more
details please visit at http://www.statistics.com/meta/.
One study reports
a half-second longer reaction time for drivers using a cell phone, while
another reports no effect from using a cell phone. Conclusions from
additional studies lie in between. How to resolve these
differences? When done right, Meta analysis can aggregate multiple
studies for a general conclusion.
Aim of the
course:
Meta-Analysis
refers to the statistical analyses that are used to synthesize summary data
from a series of studies. If the effect size (or treatment effect) is
consistent across all the studies in the synthesis, then the meta-analysis
yields a combined effect that is more precise than any of the separate
estimates, and also allows us to conclude that the effect is robust across the
kinds of studies sampled. By contrast, if the effect size (or treatment effect)
varies from one study to the next, the meta-analysis may allow us to identify
the reason for the variation and report (for example) that the treatment is
more effective in a particular kind of patient, or in a particular dose range.
In this course we
will discuss the logic of meta-analysis and the way that it is being used in
many fields, including medicine, education, social science, ecology, business,
and others. Participants will learn how to conduct a meta-analysis (how to
compute an effect size, compute summary effects, assess heterogeneity of
effects, test for differences in effect size across subgroups, and more). We
will also discuss various controversies in meta-analysis (such as the question
of mixing apples and oranges, the criticism of garbage-in-garbage-out). We will
also draw on recent headline-making analyses such as the Avandia meta-analysis.
Participants will
get hands-on experience in performing analyses using Excel(tm) and also using
Comprehensive Meta-Analysis (CMA). All participants will have access to a free
trial of CMA for the duration of the course. At the conclusion of the course,
all participants should feel comfortable conducting a meta-analysis from start
to finish using this or other software.
Who Should Take
This Course:
Researchers who
plan to perform a meta-analysis, or who want to be able understand
meta-analyses that have been published by others.
Course Program:
·
Readings in the course text (see
"Requirements" section)
·
Discussion forum with instructor
·
Homework (see below)
·
End of course data modeling project (see
below)
·
Short narrated software demos
·
Supplemental readings available online
·
Supplemental - Archive of prior course
discussions
Course outline: The course
is structured as follows
SESSION 1: Computing the Overall Effect in Meta Analysis
- What is meta analysis
o Meta analysis in various fields
o Meta analysis in medicine: Saving heart attack patients
o Meta analysis in education: Some examples
o Meta analysis in criminal justice: The "Scared Straight" jail
program
- The role of meta analysis
o In planning research
o In setting policy
- Organizations for evidence-based policy
o The Cochrane Collaboration (medicine)
o The Campbell Collaboration (social science)
- Computing a treatment effect
o Focusing on treatment effects rather than p-values
o From binary data
o From continuous data
o From correlational data
- Computing an overall effect
o Weighted means
o Basic statistics
- Forest plots
o Basic issues
SESSION 2: Fixed
vs. Random Effects in Meta Analysis
- Heterogeneity among effect sizes
o Assessing heterogeneity
- Fixed effect vs. random effects models
o Conceptual differences between these models
o Computational formulas for these models
SESSION 3: Differences
in Treatment Effects in Meta Analysis
- Understanding differences in treatment effects
o Moderator variables
o Analysis of variance
o Meta regression
- Forest Plot
o Advanced issues
SESSION 4: Publication Bias and other Issues in Meta Analysis
- Publication bias
o Funnel plots
- Multiple subgroups within studies
- Multiple outcomes within studies
- Common criticisms of meta analysis
o Apples and Oranges
o Garbage in, Garbage out
o Discrepancies between randomized trials and meta analyses
HOMEWORK:
Homework in this
course consists of short answer questions to test concepts, guided data
analysis problems using software and end of course data modeling project.
In addition to
assigned readings, this course also has an end of course data modeling project,
short narrated software demos, and supplemental readings available online.
Dr. Michael
Borenstein
(Director, Biostat, Inc.) and Hannah R. Rothstein (Professor, Baruch
College and the Graduate Center of the City University of New York) are
co-authors (with Hedges and Higgins) of “Introduction to Meta-Analysis,” and
co-editors (with Sutton) of “Publication Bias in Meta-Analysis: Prevention,
Assessment and Adjustments.”
You will be able to ask questions
and exchange comments with the instructors via a private discussion board
throughout the course. The courses take place online at
statistics.com in a series of 4 weekly lessons and assignments, and require
about 15 hours/week. Participate at your own convenience; there are no
set times when you must be online. You have the flexibility to work a bit every
day, if that is your preference, or concentrate your work in just a couple of
days.
We, the Center for eLearning and Training
(C-eLT), Pune, partner with Statistics.com and offer these courses to Indian
participants at special prices payable in INR.
Call: 020 6680 0322
Websites:
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