- Freeman tukey double arcsine transformation in r software#
- Freeman tukey double arcsine transformation in r code#
The variance of $y_i$ is then computed with $v_i = 1/(4t_i)$. Pval <- rep (NA, 10000 ) # set up vector to store p-values in for (i in 1 : 10000 ) )$, where $x_i$ denotes the total number of events that occurred and $t_i$ denotes the total person-time at risk.
K <- 5 # number of studies to simulate in each iteration
Freeman tukey double arcsine transformation in r code#
If you want to confirm this yourself, here is some basic code for a mini Monte-Carlo simulation study, demonstrating that the rejection rate is indeed equal to $\alpha =. The authors undertook a burden of disease meta-analysis to illustrate issues with the FreemanTukey double-arcsine square root transformation (FTT) when back-transformed using the harmonic mean of study sample sizes.3 Their conclusion was that the FTT method can be misleading and should be used only with caution. This is in fact the case, providing support that the rma() function is working appropriately for this scenario. To give a simple example, under the assumptions of an equal-effects model (i.e., homogeneous true effects, normally distributed effect size estimates, known sampling variances), the empirical rejection rate of $H_0: \theta = 0$ must be nominal (within the margin of error one would expect when randomly simulating such data). Third, I have conducted extensive simulation studies for many of the methods implemented in the package to ensure that their statistical properties are as one would expect based on the underlying theory. All of these examples (and some more) are also encapsulated in automated tests using the testthat package, so that any changes to the code that would lead to these examples becoming non-reproducible are automatically detected. On this website, I provide a number of such analysis examples that you can examine yourself. Second, results provided by the metafor package have been compared with published results described in articles and books (the assumption being that those results are in fact correct).
Results either agreed completely or fell within a margin of error expected when using numerical methods. Results were also compared with those provided by SAS using the proc mixed command (for more details, see van Houwelingen, Arends, & Stijnen, 2002), by SPSS using the macros developed by David Wilson (Lipsey & Wilson, 2001), by the meta (CRAN Link) and rmeta (CRAN Link) packages in R, and by Comprehensive Meta-Analysis, MetaWin, and the Review Manager of the Cochrane Collaboration. In particular, results have been compared with those provided by the metan, metareg, metabias, and metatrim commands in Stata (for more details on these commands, see Sterne, 2009). metaprop function of the R meta package to synthesize.
Freeman tukey double arcsine transformation in r software#
First of all, when corresponding analyses could be carried out, I have compared the results provided by the metafor package with those obtained with other software packages for several data sets. used Freeman-Tukey double arcsine transformation method for meta-analysis of pooled proportion. Various attempts have been made to validate the functions in the metafor package.
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