Differences

This shows you the differences between two versions of the page.

--- analyses:vanhouwelingen2002 [2021/10/22 15:29] – Wolfgang Viechtbauer
+++ analyses:vanhouwelingen2002 [2021/11/08 15:13] – Wolfgang Viechtbauer
@@ Line 39: / Line 39: @@
 </code>
-==== Fixed-Effects Model ====
+==== Equal-Effects Model ====
-The first model considered is the fixed-effects model based on the log odds ratios. The same model can be fitted with:
+The first model considered is the equal-effects model based on the log odds ratios. The same model can be fitted with:
 <code rsplus>
-res <- rma(yi, vi, data=dat, method="FE")
+res <- rma(yi, vi, data=dat, method="EE")
 res
 </code>
 <code output>
-Fixed-Effects Model (k = 13)
+Equal-Effects Model (k = 13)
-Test for Heterogeneity:
+I^2 (total heterogeneity / total variability):   92.65%
+H^2 (total variability / sampling variability):  13.60
+Test for Heterogeneity:
 Q(df = 12) = 163.1649, p-val < .0001
 Model Results:
-estimate       se     zval     pval    ci.lb    ci.ub
+estimate      se      zval    pval    ci.lb    ci.ub
- -0.4361   0.0423 -10.3190   <.0001  -0.5190  -0.3533      ***
+ -0.4361  0.0423  -10.3190  <.0001  -0.5190  -0.3533  ***
 ---
-Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 </code>
 For easier interpretation, these results can be back-transformed to the odds ratio scale with:
@@ Line 293: / Line 296: @@
 Then Figure 5 in the paper (p. 605) can be recreated with:
 <code rsplus>
-tmp <- rma(measure="OR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat, method="FE")
+tmp <- rma(measure="OR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat, method="EE")
 labbe(tmp, xlim=c(-8,0), ylim=c(-8,0), xlab="ln(odds) not-vaccinated group", ylab="ln(odds) vaccinated group", lty=c("dotted", "blank"), grid=TRUE)
 points(coef(res)[1], coef(res)[2], pch=19, cex=3)