Before pooling proportions (eg. prevalence rates), one needs to do a
heterogeneity analysis to examine whether the proportions from the different
sources are appropriately similar. This is discussed in Chapter 9 of Fleiss
(Statistical methods for rates and proportions. 2nd edition, 1981, John
Wiley & Sons, New York, Chichester). Fleiss uses a Chi Square test for m
proportions. If heterogeneity is found, then it may be appropriate to group
according to the variable that appears to be causing the differences between
the proportions. If there is not enough information to arrive at such a
variable, then it may be more appropriate to simply average the proportions
rather than pool them. This gives a mean proportion unweighted by study
size. Unfortunately I don't know how to get a confidence limit for this
unweighted mean.
This is the fixed effects versus random effects problem. What we need is a
random effects method to get an unweighted mean and CL for a set of single
proportions. All of the meta-analytic methods I can find are for combining
studies that provide a difference between two proportions (the effect size).
If anyone knows of random effects methods for series of single proportions
please let us know. This has come up before on the list without resolution.
David L. Doggett, Ph.D.
Senior Medical Research Analyst
Health Technology Assessment and Information Services
ECRI, a non-profit health services research organization
5200 Butler Pike
Plymouth Meeting, Pennsylvania 19462, U.S.A.
Phone: (610) 825-6000 x5509
FAX: (610) 834-1275
http://www.ecri.org
e-mail: [log in to unmask]
-----Original Message-----
From: [log in to unmask] [mailto:[log in to unmask]]
Sent: Thursday, July 26, 2001 11:51 AM
To: [log in to unmask]
Subject: need help with method for summarizing prevalence rates
If one is looking to summarize prevalence rates from different studies in a
single summarized rate(Aware of the controversy of doing such a thing in
the first place), is there a conventional way of doing this. My guess would
be to simply add the numerators and denominators and calculate the standard
error of the summary rate. This would give more weight to larger studies,
which seems appropriate.
Thanks
Paul Waraich
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