Description Usage Arguments Details Value Author(s) References Examples
View source: R/NMAoutlier.measures.R
This function calculates several (simple or/and deletion) measures for detection of outliers and influential studies in network metaanalysis.
Outlier and influential detection measures are:
Simple outlier and influential measures for each study (Raw residuals, Standardized residuals, Studentized residuals, Mahalanobis distance, leverage).
Outlier and influential
deletion measures for each study (Shift the mean) (Raw deleted
residuals, Standardized deleted residuals, Studentized deleted
residuals, Cook distance between the treatment estimates for study
j and treatment estimates when study j is removed; Ratio of
determinants of variancecovariance matrix of treatment estimates
for study j to treatment estimates when study j is removed; weight
leave one out;leverage leave one out; heterogeneity estimator leave
one out; R statistic for heterogeneity; R statistic for Q
(Qtotal
), R statistic for heterogeneity Q (Qhet
), R
statistic for Qinconsistency (Qinc
), DFbetas.)
1 2 3 4 5 6 7 8 9 10 11 12  NMAoutlier.measures(
TE,
seTE,
treat1,
treat2,
studlab,
data = NULL,
sm,
reference = "",
measure = "simple",
...
)

TE 
Estimate of treatment effect, i.e. difference between first and second treatment (e.g. log odds ratio, mean difference, or log hazard ratio). 
seTE 
Standard error of treatment estimate. 
treat1 
Label/Number for first treatment. 
treat2 
Label/Number for second treatment. 
studlab 
Study labels (important when multi arm studies are included). 
data 
A data frame containing the study information. 
sm 
A character string indicating underlying summary measure,
e.g., 
reference 
Reference treatment group. 
measure 
Outlier and influential detection measures, simple measures (default: "simple") or outlier and influential detection measures considered study deletion (measure = "deletion"). 
... 
Additional arguments passed on to

Outlier and influential detection measures (simple or deletion) for network metaanalysis.
Network metaanalysis from graphtheory [Rücker, 2012] is fitted
with (netmeta
function) of R package netmeta [Rücker et al., 2015].
A description of the outlier and influential detection measures in the context of network metaanalysis can be found in Petropoulou (2020).
Let n be the number of treatments in a network and let
m be the number of pairwise treatment comparisons. If there
are only twoarm studies, m is the number of studies. Let
TE
and seTE
be the vectors of observed effects and their standard
errors. Comparisons belonging to multiarm studies are identified
by identical study labels (argument studlab
).
This function calculates outlier and influential detection measures for each study.
Simple outlier and influential measures (measure
= "simple") are:
Raw residuals, Standardized residuals, Studentized residuals, Mahalanobis distance
and leverage for each study.
For deletion outlier and influential measures (measure
= "deletion"):
Standardized deleted residual; Studentized deleted residual; Cook distance between the treatment estimates for study j
and treatment estimates when study j is removed;
Ratio of determinants of variancecovariance matrix of treatment estimates for study j to treatment estimates when study j is removed;
Weight leave one out;leverage leave one out; heterogeneity estimator leave one out;
R statistic for heterogeneity; R statistis for estimates; R statistic for Q (Qtotal
), R statistic for heterogeneity Q
(Qhet
), R statistic for Qinconsistency (Qinc
), DFbetas.
An object of class NMAoutlier.measures
;
with a list containing the following components when choosing simple measures:
dat 
Matrix containing the data 
eraw 
Raw residual for each study included in the network. 
estand 
Standardized residual for each study included in the network. 
estud 
Studentized residual for each study included in the network. 
Mah 
Mahalanobis distance for each pairwise comparison. 
Mah.distance 
Mahalanobis distance for each study included in the network. 
leverage 
Leverage for each study included in the network. 
measure 
type of measure used. 
call 
Function call 
a list containing the following components,when choosing deletion measures:
dat 
Matrix containing the data 
eraw.deleted 
Raw deleted residual for each study included in the network. 
estand.deleted 
Standardized deleted residual for each study included in the network. 
estud.deleted 
Studentized deleted residual for each study included in the network. 
Cooks.distance 
Cook distance between the treatment estimates for study j and treatment estimates when study j is removed 
Covratio 
Ratio of determinants of variancecovariance matrix of treatment estimates for study j to treatment estimates when study j is removed. 
w.leaveoneout 
Weight leave one out. 
H.leaveoneout 
Leverage leave one out. 
heterog.leaveoneout 
Heterogeneity estimator leave one out. 
Rheterogeneity 
R statistic for heterogeneity. 
Restimates 
R statistis for estimates. 
RQtotal 
R statistic for Q ( 
RQhet 
R statistic for heterogeneity Q ( 
RQinc 
R statistic for Qinconsistency ( 
DFbetas 
DFbetas. 
measure 
type of measure used. 
call 
Function call 
Maria Petropoulou <petropoulou@imbi.unifreiburg.de>
Rücker G (2012): Network metaanalysis, electrical networks and graph theory. Research Synthesis Methods, 3, 312–24
Rücker G, Schwarzer G (2015): Ranking treatments in frequentist network metaanalysis works without resampling methods. BMC Medical Research Methodology, 15, 58
Petropoulou M (2020): Exploring methodological challenges in network metaanalysis models and developing methodology for outlier detection. PhD dissertation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  data(smokingcessation, package = "netmeta")
smokingcessation$id < 1:nrow(smokingcessation)
study912 < subset(smokingcessation, id %in% 9:12)
p1 < netmeta::pairwise(list(treat1, treat2, treat3),
list(event1, event2, event3),
list(n1, n2, n3),
data = study912,
sm = "OR")
# Outlier and influential detection measures for studies 9, 10, 11, 12
meas < NMAoutlier.measures(p1)
# Standardized residual for each study included in the network
meas$estand
## Not run:
# Outlier and influential deletion measures for studies 9, 10, 11, 12.
delete < NMAoutlier.measures(p1, measure = "deletion")
# Standardized deleted residual for studies 9, 10, 11, 12.
delete$estand.deleted
data(smokingcessation, package = "netmeta")
# Transform data from armbased to contrastbased format
# We use 'sm' argument for odds ratios.
# We use function pairwise from netmeta package
#
p1 < netmeta::pairwise(list(treat1, treat2, treat3),
list(event1, event2, event3),
list(n1, n2, n3),
data = smokingcessation,
sm = "OR")
# Outlier and influential detection measures for each study in the network
meas < NMAoutlier.measures(p1, measure = "simple")
# Mahalanobis distance for each study included in the network
meas$Mah
## End(Not run)

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