[R-meta] [metafor] Mantel-Haenszel method - rma.mh vs meta(metabin)
m@rc@h@rms m@iii@g oii mh-@@@iytics@eu
m@rc@h@rms m@iii@g oii mh-@@@iytics@eu
Mon Jan 30 20:30:33 CET 2023
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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