| Name : R-PubBias
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| Version : 1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.2.13
| Date : 2024-06-14 11:34:34
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| Group : Development/Libraries/Other
| Source RPM : R-PubBias-1.0-lp153.2.13.src.rpm
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| Size : 0.04 MB
| |
| Packager : (none)
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| Summary : Performs simulation study to look for publication bias, using a technique described by Ioannidis and Trikalinos; Clin Trials. 2007;4(3):245-53.
|
Description :
I adapted a method designed by Ioannidis and Trikalinos, which compares
the observed number of positive studies in a meta-analysis with the
expected number, if the summary measure of effect, averaged over the
individual studies, were assumed true. Excess in the observed number of
positive studies, compared to the expected, is taken as evidence of
publication bias. The observed number of positive studies, at a given
level for statistical significance, is calculated by applying Fisher\'s
exact test to the reported 2x2 table data of each constituent study,
doubling the Fisher one-sided P-value to make a two-sided test. The
corresponding expected number of positive studies was obtained by
summing the statistical powers of each study. The statistical power
depended on a given measure of effect which, here, was the pooled odds
ratio of the meta-analysis was used. By simulating each constituent
study, with the given odds ratio, and the same number of treated and
non-treated as in the real study, the power of the study is estimated
as the proportion of simulated studies that are positive, again by a
Fisher\'s exact test. The simulated number of events in the treated and
untreated groups was done with binomial sampling. In the untreated
group, the binomial proportion was the percentage of actual events
reported in the study and, in the treated group, the binomial sampling
proportion was the untreated percentage multiplied by the risk ratio
which was derived from the assumed common odds ratio. The statistical
significance for judging a positive study may be varied and large
differences between expected and observed number of positive studies
around the level of 0.05 significance constitutes evidence of
publication bias. The difference between the observed and expected is
tested by chi-square. A chi-square test P-value for the difference
below 0.05 is suggestive of publication bias, however, a less stringent
level of 0.1 is often used in studies of publication bias as the number
of published studies is usually small.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
