[R-meta] Zero seTE in network MA
Guido Schwarzer
sc at imbi.uni-freiburg.de
Wed Sep 20 12:57:45 CEST 2017
Dear Carla,
I think this is a misunderstanding.
R function netmeta() works perfectly with zero standard errors (by
excluding pairwise comparisons with zero or missing standard errors from
the network meta-analysis). This is the same behaviour as in "basic"
meta-analysis of pairwise comparisons (see metagen() in R package
*meta*). I attached a corresponding fictitious example for netmeta() to
this email.
The new checks in *netmeta*, version 0.9-6, you are referring to,
related for /treatment arm variances in multi-arm studies/ which are
calculated internally. Sometimes, users enter standard errors for
pairwise comparisons in multi-arm studies that simply do not "add up".
Accordingly, treatment arm variances can be negative.
Maybe somebody else can also comment on your idea to use a (very) small
standard error for a pairwise comparison with zero standard error. In my
view, the problem with such an approach is that this study gets a very
large weight in the (network) meta-analysis.
Best wishes,
Guido
--
Dr. Guido Schwarzer (sc at imbi.uni-freiburg.de)
Institute for Medical Biometry and Statistics
Stefan-Meier-Str. 26, D-79104 Freiburg | Phone: +49 (0)761 203 6668
http://www.imbi.uni-freiburg.de | Fax: +49 (0)761 203 6680
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library(netmeta)
p1 <- pairwise(treat = c(LETTERS[1:3], LETTERS[2:3]),
n = rep(10, 5), mean = 1:5, sd = 0:4,
studlab = c(rep("Study 1", 3), rep("Study 2", 2)))
netmeta(p1, ref = "C")
p1$seTE[is.na(p1$seTE)] <- 0
netmeta(p1, ref = "C")
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R version 3.4.1 (2017-06-30) -- "Single Candle"
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Type 'demo()' for some demos, 'help()' for on-line help, or
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[Previously saved workspace restored]
> library(netmeta)
Loading required package: meta
Loading 'meta' package (version 4.8-4).
Type 'help(meta)' for a brief overview.
Loading 'netmeta' package (version 0.9-6).
Type 'help(netmeta-package)' for a brief overview.
>
> p1 <- pairwise(treat = c(LETTERS[1:3], LETTERS[2:3]),
+ n = rep(10, 5), mean = 1:5, sd = 0:4,
+ studlab = c(rep("Study 1", 3), rep("Study 2", 2)))
Comparisons will not be considered in network meta-analysis:
TE seTE studlab treat1 treat2 n1 mean1 sd1 n2 mean2 sd2
-1 NA Study 1 A B 10 1 0 10 2 1
-2 NA Study 1 A C 10 1 0 10 3 2
Warning messages:
1: In metacont(dat$n1, dat$mean1, dat$sd1, dat$n2, dat$mean2, dat$sd2, :
Studies with non-positive values for sd.e or sd.c get no weight in meta-analysis.
2: In metacont(dat$n1, dat$mean1, dat$sd1, dat$n2, dat$mean2, dat$sd2, :
Studies with non-positive values for sd.e or sd.c get no weight in meta-analysis.
3: Comparisons with missing TE / seTE or zero seTE will not be considered in network meta-analysis.
>
> netmeta(p1, ref = "C")
Comparisons not considered in network meta-analysis:
studlab treat1 treat2 TE seTE
Study 1 A B -1 NA
Study 1 A C -2 NA
Original data:
treat1 treat2 TE seTE
Study 1 B C -1 0.7071
Study 2 B C -1 1.5811
Number of treatment arms (by study):
narms
Study 1 2
Study 2 2
Results (fixed effect model):
treat1 treat2 MD 95%-CI Q leverage
Study 1 B C -1 [-2.2652; 0.2652] 0 0.83
Study 2 B C -1 [-2.2652; 0.2652] 0 0.17
Number of studies: k = 2
Number of treatments: n = 2
Number of pairwise comparisons: m = 2
Number of designs: d = 1
Fixed effect model
Treatment estimate (sm = 'MD', comparison: 'B' vs 'C'):
MD 95%-CI
B -1 [-2.2652; 0.2652]
C . .
Quantifying heterogeneity / inconsistency:
tau^2 = 0; I^2 = 0%
Test of heterogeneity / inconsistency:
Q d.f. p-value
0 1 1.0000
Warning messages:
1: Comparisons with missing TE / seTE or zero seTE not considered in network meta-analysis.
2: Only a single design in network meta-analysis.
>
> p1$seTE[is.na(p1$seTE)] <- 0
>
> netmeta(p1, ref = "C")
Comparisons not considered in network meta-analysis:
studlab treat1 treat2 TE seTE
Study 1 A B -1 0
Study 1 A C -2 0
Original data:
treat1 treat2 TE seTE
Study 1 B C -1 0.7071
Study 2 B C -1 1.5811
Number of treatment arms (by study):
narms
Study 1 2
Study 2 2
Results (fixed effect model):
treat1 treat2 MD 95%-CI Q leverage
Study 1 B C -1 [-2.2652; 0.2652] 0 0.83
Study 2 B C -1 [-2.2652; 0.2652] 0 0.17
Number of studies: k = 2
Number of treatments: n = 2
Number of pairwise comparisons: m = 2
Number of designs: d = 1
Fixed effect model
Treatment estimate (sm = 'MD', comparison: 'B' vs 'C'):
MD 95%-CI
B -1 [-2.2652; 0.2652]
C . .
Quantifying heterogeneity / inconsistency:
tau^2 = 0; I^2 = 0%
Test of heterogeneity / inconsistency:
Q d.f. p-value
0 1 1.0000
Warning messages:
1: Comparisons with missing TE / seTE or zero seTE not considered in network meta-analysis.
2: Only a single design in network meta-analysis.
>
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