Message-ID: <CAJn_xx=_x4_TTR-DGTaMO_ocqjjRm1R79UFLHXc0v2Fhywi1+Q@mail.gmail.com>
Date: 2018-07-29T19:25:10Z
From: Howard Friedman
Subject: [R-meta] Question about inverse variance weighting using the Metafor package
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
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