[R-meta] Back transformation of double arscine transformed estimates in metafor
Dear Wolfgang
The variable age_cor has 6 levels (ref. level 5)
table(a$age_cor)
5 1 2 3 4 missing
111 140 27 113 19 8
With your code suggestion slightly modified
b<-rma.glmm(xi=compl_treat, ni=total, mods = ~age_cor, measure = "PLO",
data=a)
print(b)
Mixed-Effects Model (k = 401; tau^2 estimator: ML)
tau^2 (estimated amount of residual heterogeneity): 1.8327
tau (square root of estimated tau^2 value): 1.3538
I^2 (residual heterogeneity / unaccounted variability): 98.91%
H^2 (unaccounted variability / sampling variability): 91.85
Tests for Residual Heterogeneity:
Wld(df = 395) = 4777257347008370311248.0000, p-val < .0001
LRT(df = 395) = 0.0000, p-val = 1.0000
Test of Moderators (coefficient(s) 2:6):
QM(df = 5) = 20.3959, p-val = 0.0011
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt -3.9819 0.1456 -27.3422 <.0001 -4.2674 -3.6965 ***
age_cor1 0.3358 0.1922 1.7474 0.0806 -0.0408 0.7124 .
age_cor2 0.3169 0.3093 1.0244 0.3057 -0.2894 0.9231
age_cor3 0.8528 0.2012 4.2397 <.0001 0.4586 1.2471 ***
age_cor4 -0.0370 0.3850 -0.0962 0.9234 -0.7916 0.7176
age_cormissing 0.0009 0.5648 0.0016 0.9987 -1.1061 1.1080
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
c<-predict(b, newmods=rbind(0, diag(5)), transf=transf.ilogit)
print(c)
pred ci.lb ci.ub cr.lb cr.ub
1 0.0183 0.0138 0.0242 0.0013 0.2119
2 0.0254 0.0200 0.0323 0.0018 0.2726
3 0.0250 0.0148 0.0419 0.0017 0.2772
4 0.0419 0.0322 0.0544 0.0030 0.3866
5 0.0177 0.0089 0.0349 0.0012 0.2184
6 0.0183 0.0064 0.0516 0.0011 0.2460
- Is the interpretation that line 1 represents the age_cor reference
level (level 5) proportion and the remaining levels as listed in print (b)?
- If I want to explore age_cor further, can I add multiple moderators to
the model and just increase the diag in predict?
Regards,
Daniel
Daniel M?nsted Shabanzadeh
MD, PhD
Department of Gastroenterology, Surgical Unit
Hvidovre Hospital
Mobile +45 2546 5251
On Sat, Oct 5, 2019 at 2:16 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Dear Daniel, If level 5 is the reference level, then that is what the intercept represents, so the 0.0183 cannot represent level 5. You would have to provide the output of 'b' for me to tell you better what is being estimated here, but 0.0183 is the estimated proportion for whatever level the last coefficient represents in the model. If you want all estimated proportions for all 6 levels, then you can get this with a single command: predict(b, newmods=rbind(0, diag(5)), transf=transf.ilogit) The first will be for the reference level, the rest for each other level. Best, Wolfgang -----Original Message----- From: Daniel M?nsted Shabanzadeh [mailto:dmshaban at gmail.com] Sent: Saturday, 05 October, 2019 12:31 To: Viechtbauer, Wolfgang (SP) Cc: r-sig-meta-analysis at r-project.org Subject: Re: [R-meta] Back transformation of double arscine transformed estimates in metafor Dear Wolfgang I have now run the models, but still seem to have some conversion problems when trying to obtain proportions from the regression model. The variable age_cor is categorical with 6 levels (level 5 is ref.). b<-rma.glmm(xi=compl_treat, ni=total, mods = ~age_cor, measure = "PLO", data=a) c<-predict(b, newmods=c(0,0,0,0,1), transf=transf.ilogit) print(c) pred ci.lb ci.ub cr.lb cr.ub 0.0183 0.0064 0.0516 0.0011 0.2460 As far as I interpretate this results, it means that if age_cor is fixed at 0 in level 1-4 and level 5 is fixed at 1, the proportion is 0.0183. Is it not possible to obtain proportions from all levels in the variabel when one level is the reference? Like the case in studies with relative risks exploring multiple level categorical variables with one reference level. Regards, Daniel Daniel M?nsted Shabanzadeh MD, PhD Department of Gastroenterology, Surgical Unit Hvidovre Hospital Mobile +45 2546 5251