Note that the meta-analysis is done using the log odds ratios. Standard Error is the standard deviation of the sampling distribution of a statistic. H^2 (total variability / within-study variance): 1.00 I^2 (% of total variability due to heterogeneity): 0.00%
Tau (sqrt of the estimate of total heterogeneity): 0 The next number is the bootstrap estimate of bias (which weve not gone into here), and lastly the bootstrap estimated standard error, which here is 0.972. Tau^2 (estimate of total amount of heterogeneity): 0 (SE = 0.0046) Random-Effects Model (k = 2 tau^2 estimator: REML) Res <- rma(yi=yi, sei=sei) # fit a random-effects model to these data Use the SD function (standard deviation in R ) for standalone computations.
How to calculate standard error of the estimate code#
I'll illustrate how you can do the computations with R, using the metafor package: library(metafor) You can easily calculate the standard error of the true mean using functions contained within the base R code package. Now you have everything to do a meta-analysis. So, it follows that $SE = log(OR) / z$, which yields $SE = 0.071$ for the first and $SE =. These z-values are actually the test statistics calculated by taking the log of the odds ratios divided by the corresponding standard errors (i.e., $z = log(OR) / SE$). So how do we report our findings for our best estimate of this elusive true value To calculate the standard deviation for a sample of N measurements.
007$, this is $z = -2.457$ (they are negative, since the odds ratios are below 1). Let’s see how we can make use of this fact to recognize OLS estimators in disguise as more general GMM estimators. 0115$, this is $z = -2.273$ and for $p =. this is a unique characterization of the OLS estimate. Then convert these p-values to the corresponding z-values. First, convert the two-sided p-values into one-sided p-values by dividing them by 2. You can calculate/approximate the standard errors via the p-values.
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