[R-meta] Question about inverse variance weighting using the Metafor package

[Howard Friedman]

I am using the Metafor package for the first time.  I read the
documentation and wanted to confirm that I am doing the correct steps to
computing the weighted mean difference where the weights are inverse
variance.

My data set has inputs for each study of n_control, n_treatment,
mean_controls, mean_treatment, sd_controls, and sd_treatment.  Am I correct
that to do the inverse weighting I need to do the following:

(1)    Compute the pooled variance by defining variance= sd_controls^2+
sd_treatment^2

(2)    Define my variables as below:

fig_1 <- escalc(n1i = n_controls, n2i = n_treatment, m1i = mean_controls,
m2i = mean_ treatment,  sd1i = sd_controls, sd2i = sd_ treatment, data =
fig_1, measure = "MD", append = TRUE)

(3)    Then for my fixed effects model weighting by 1/variance, run

rma(yi, vi, method="FE",weights=1/var_total,data=fig_1)

(4)    And for my variable effects model weighted by 1/variance, run

rma(yi, vi, weights=1/var_total,data=fig_1)

Appreciate you feedback or corrections on this approach.



Thank you,

Howard

-- 


Columbia University School of International and Public Affairs; School of
Public Health
www.linkedin.com/in/howard-friedman-590ba8
www.Howard-Friedman.com

	[[alternative HTML version deleted]]