[R-meta] [metafor] Mantel-Haenszel method - rma.mh vs meta(metabin)

[m@rc@h@rms m@iii@g oii mh-@@@iytics@eu]

Hi all,

 

I have the following scenario: 2x2 table, binary data

 

Multiple studies are comparing the SLN detection in breast cancer patients
of two different detection methods (ICG vs RI).

The literature review is providing 13 studies, table is attached. 

 

Aim is to calculate the risk difference. Since the detection rate is a
binary outcome, the inverse variance method might not be a good choice for
calculating the weights. Therefore, I wanted to use the Mantel-Haenszel
method. 

 

When I am using the metabin function from [meta], I can calculate the pooled
effect as follows:

 

m.sln<- metabin(event.e = ICG_SLN, 

                 n.e = ICG_total,

                 event.c = RI_SLN,

                 n.c = RI_total,

                 studlab = Study,

                 data = df,

                 sm = "RD",

                 method = "MH",

                 MH.exact = TRUE,

                 fixed = FALSE,

                 random = TRUE,

                 method.tau = "PM",

                 hakn = TRUE,

                 title = "SLN detection rate")

 

My problem: I am not able to run this utilizing the rma.mh function in
[metafor] for RD. The outcomes are different:

 

[META]



                         RD            95%-CI    t p-value

Random effects model 0.0112 [-0.0224; 0.0447] 0.73  0.4821

 

Quantifying heterogeneity:

tau^2 = 0.0021 [0.0004; 0.0092]; tau = 0.0455 [0.0197; 0.0960]

I^2 = 63.2% [33.0%; 79.7%]; H = 1.65 [1.22; 2.22]

 

Test of heterogeneity:

     Q d.f. p-value

32.57   12  0.0011

 

dat <- escalc(measure = "RD", 

              ai = ICG_pos, ci = RI_pos, 

              n1i = ICG_total, n2i = RI_total,

              slab = paste(Study, Year, sep = ", "),

 

                                              )

res <- rma(dat, method="REML")

[METAFOR] - rma, random effects
Random-Effects Model (k = 13; tau^2 estimator: REML)
 
  logLik  deviance       AIC       BIC      AICc   
 17.5702  -35.1404  -31.1404  -30.1705  -29.8070   
 
tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
tau (square root of estimated tau^2 value):      0.0355
I^2 (total heterogeneity / total variability):   73.23%
H^2 (total variability / sampling variability):  3.74
 
Test for Heterogeneity:
Q(df = 12) = 31.9726, p-val = 0.0014
 
Model Results:
 
estimate      se    zval    pval    ci.lb   ci.ub    
  0.0111  0.0129  0.8631  0.3881  -0.0141  0.0364 

 

res_1 <- rma.mh(measure = "RD",

                ai = ICG_pos, ci = RI_pos, 

                n1i = ICG_total, n2i = RI_total, data=dat)

[METAFOR] rma.mh
Equal-Effects Model (k = 13)
 
  logLik  deviance       AIC       BIC      AICc   
 18.3474   32.0734  -34.6948  -34.1299  -34.3312   
 
I^2 (total heterogeneity / total variability):  62.59%
H^2 (total variability / sampling variability): 2.67
 
Test for Heterogeneity: 
Q(df = 12) = 32.0734, p-val = 0.0013
 
Model Results:
 
estimate      se    zval    pval   ci.lb   ci.ub 
  0.0119  0.0058  2.0411  0.0412  0.0005  0.0234

 

 

 

hanks, Marc


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