How to perform complex meta-analyses using R?

Performing a complex meta-analysis in R might sound tough, but don't worry! You can do it by following these simple steps:

1. Install and Load Necessary Packages: To start off, R has some amazing packages that can help you with meta-analysis. You will primarily need 'meta' and 'metafor'. To install them, you type this in your R console:

install.packages("meta")
install.packages("metafor")

Then, load them with:

library(meta)
library(metafor)

2. Gather Your Data: Before we dive into the analysis, make sure you have all your study data ready. This usually includes the effect sizes, standard errors or confidence intervals, and sample sizes of the studies you're analyzing.

3. Prepare Your Data: Create a data frame in R that holds this information. A data frame is like a spreadsheet but in R. You can do this by using a command like:

data <- data.frame(
study = c("Study1", "Study2", "Study3"),
effect_size = c(0.5, 0.6, 0.7),
se_effect_size = c(0.1, 0.15, 0.2)
)

In this example, "study" names each study, "effect_size" is the measured effect, and "se_effect_size" is the standard error of each effect size.

4. Run the Meta-Analysis: With your data prepared, it's time to run the analysis. If you're using the 'metafor' package, a basic model would look like:

res <- rma(yi = effect_size, sei = se_effect_size, data = data)

In this example, yi represents your effect sizes, and sei are the standard errors. The function 'rma()' runs the meta-analysis model.

5. Examine the Results: After running the model, you can look at the results:

summary(res)

This summary will give you a lot of useful information, like the overall effect size, confidence intervals, and whether the effect is statistically significant or not.

6. Check for Heterogeneity: Heterogeneity tells you how different the studies are from each other. In a meta-analysis, you can check this with:

forest(res) # To visualize the variability among studies
test for heterogeneity, using Q and I^2 statistics
heterogeneity(res)

7. Perform Subgroup Analysis or Meta-Regression: If you have different groups of studies or variables that might affect the outcomes, you might want to explore further:

subgroup analysis: by categorizing your studies based on a characteristic and running separate meta-analyses.
meta-regression: by adding moderator variables to your model.
For subgroup analysis:

res.subgroups <- rma(yi, vi, mods = ~ subgroup - 1, data=data)
summary(res.subgroups)

For meta-regression:

res.metareg <- rma(yi, vi, mods = ~ continuous.mod - 1, data=data)
summary(res.metareg)

In these commands, 'subgroup' is a categorical variable (like "age group"), and 'continuous.mod' is a continuous variable (like "year").

8. Check for Publication Bias: To see if there's a bias in the published studies versus what might be out there but not published, use:

funnel(res) # To plot a funnel plot
ranktest(res) # For a statistical test of bias

And there you go! You've just performed a complex meta-analysis in R. Remember, each analysis is unique, so your results will depend on your data and the specifics of your research question. Practice makes perfect, so keep experimenting with different options and parameters!