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analyses:vanhouwelingen2002 [2021/10/22 15:29] – Wolfgang Viechtbaueranalyses:vanhouwelingen2002 [2022/08/03 11:23] – Wolfgang Viechtbauer
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 </code> </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> <code rsplus>
-res <- rma(yi, vi, data=dat, method="FE")+res <- rma(yi, vi, data=dat, method="EE")
 res res
 </code> </code>
 <code output> <code output>
-Fixed-Effects Model (k = 13)+Equal-Effects Model (k = 13) 
 + 
 +I^2 (total heterogeneity / total variability):   92.65% 
 +H^2 (total variability / sampling variability):  13.60
  
 Test for Heterogeneity: Test for Heterogeneity:
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 Model Results: 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> </code>
 For easier interpretation, these results can be back-transformed to the odds ratio scale with: For easier interpretation, these results can be back-transformed to the odds ratio scale with:
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 </code> </code>
 <code output> <code output>
-       estimate   ci.lb   ci.ub  +       estimate   ci.lb   ci.ub 
-tau^2    0.3025  0.1151  0.8937  +tau^2    0.3025  0.1151  0.8937 
-tau      0.5500  0.3393  0.9454  +tau      0.5500  0.3393  0.9454 
-I^2(%)  91.2283 79.8303 96.8485 +I^2(%)  91.2283 79.8303 96.8485
 H^2     11.4003  4.9579 31.7310 H^2     11.4003  4.9579 31.7310
 </code> </code>
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 <code rsplus> <code rsplus>
 res <- rma.mv(yi, vi, mods = ~ group - 1, random = ~ group | trial, struct="UN", data=dat.long, method="ML") res <- rma.mv(yi, vi, mods = ~ group - 1, random = ~ group | trial, struct="UN", data=dat.long, method="ML")
-reg <- matreg(y=2, x=1, R=res$G, cov=TRUE, means=coef(res), n=res$g.levels.comb.k) +reg <- matreg(y=2, x=1, R=res$G, cov=TRUE, means=coef(res), n=res$g.levels.comb.k)
 reg reg
 </code> </code>
 <code output> <code output>
-         estimate      se     tval  df    pval    ci.lb    ci.ub  +         estimate      se     tval  df    pval    ci.lb    ci.ub 
-intrcpt   -1.8437  0.3265  -5.6477  11  0.0001  -2.5623  -1.1252  ***  +intrcpt   -1.8437  0.3265  -5.6477  11  0.0001  -2.5623  -1.1252  *** 
-CON        0.7300  0.0749   9.7467  11  <.0001   0.5651   0.8948  *** +CON        0.7300  0.0749   9.7467  11  <.0001   0.5651   0.8948  ***
  
 --- ---
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 Then Figure 5 in the paper (p. 605) can be recreated with: Then Figure 5 in the paper (p. 605) can be recreated with:
 <code rsplus> <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) 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) points(coef(res)[1], coef(res)[2], pch=19, cex=3)
analyses/vanhouwelingen2002.txt · Last modified: 2022/08/03 17:52 by Wolfgang Viechtbauer