[R-meta] Back transformation of double arscine transformed estimates in metafor

[Viechtbauer, Wolfgang (SP)]

- Is the interpretation that line 1 represents the age_cor reference level (level 5) proportion and the remaining levels as listed in print (b)? 

Yes!

- If I want to explore age_cor further, can I add multiple moderators to the model and just increase the diag in predict?

Do you mean: More levels of age_cor? Then yes. If you mean adding additional moderators, then it depends on what kind of moderators you are adding. They may not be dummy variables.

These might be useful readings:

http://www.metafor-project.org/doku.php/tips:testing_factors_lincoms

http://www.metafor-project.org/doku.php/tips:multiple_factors_interactions

Best,
Wolfgang

-----Original Message-----
From: Daniel Mønsted Shabanzadeh [mailto:dmshaban using gmail.com] 
Sent: Saturday, 05 October, 2019 17:56
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis using r-project.org
Subject: Re: [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 Mønsted Shabanzadeh
MD, PhD
Department of Gastroenterology, Surgical Unit
Hvidovre Hospital
Mobile +45 2546 5251