LIKE:)
On 2012-4-5 15:03, Viechtbauer Wolfgang (STAT) wrote:
I do not see any major difficulties with this case either. Suppose you have OR
= 1.5 (with 95% CI: 1.19 to 1.90) indicating that the odds of a particular
outcome (e.g., disease) is 1.5 times greater when the (continuous) exposure
variable increases by one unit. Then you can back-calculate the SE of log(OR) =
.41 with
sei = (ln(ci.ub) - ln(ci.lb)) / (2*1.96),
which in this case is approximately 0.12. The sampling variance of log(OR) is
then vi = sei^2.
Now you have everything for the meta-analysis, using any of the packages
mentioned.
What Thomas already mentioned is that the 'one unit increase' must mean the
same thing in each study. Therefore, if the exposure variable is measured in
months in one study and in years in another study, then the odds ratios are
obviously not directly comparable. If the units are just multiples of each
other, then you can easily calculate what the OR would be when putting the
exposure variable on the same scale. For example, an OR of 1.5 for a one month
increase in exposure is the same as an OR of 1.5^12 = 129.75 for a one year
increase in exposure.
Best,
Wolfgang
--
Wolfgang Viechtbauer, Ph.D., Statistician
Department of Psychiatry and Psychology
School for Mental Health and Neuroscience
Faculty of Health, Medicine, and Life Sciences
Maastricht University, P.O. Box 616 (VIJV1)
6200 MD Maastricht, The Netherlands
+31 (43) 388-4170 | http://www.wvbauer.com
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On Behalf Of Thomas Lumley
Sent: Wednesday, April 04, 2012 23:42
To: Marie-Pierre Sylvestre
Cc: r-help@r-project.org
Subject: Re: [R] meta-analysis, outcome = OR associated with a continuous
independent variable
On Thu, Apr 5, 2012 at 8:24 AM, Marie-Pierre Sylvestre
<mp.sylves...@gmail.com> wrote:
Hello everyone,
I want to do a meta-analysis of case-control studies on which an OR
was computed based on a continuous exposure. I have found several
several packages (metafor, rmeta, meta) but unless I misunderstood
their main functions, it seems to me that they focus on two-group
comparisons (binary independent variable), and do not have the option
of using a continuous independent variable.
There's no problem in using continuous exposures in meta.summaries() in
the rmeta package. For each study, compute your log odds ratio and its
standard error, and feed them in.
You just need to make sure that the odds ratio is in the same units in
each study, of course.
-thomas
--
Thomas Lumley
Professor of Biostatistics
University of Auckland
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