measplot: Plot(s) to monitor selected outlier and influential...
In NMAoutlier: Detecting Outliers in Network Meta-Analysis

Description Usage Arguments Details Author(s) Examples

This function generates plot(s) of the selected outlier detection measure(s) for each study included in the network. Candidate statistics to be monitored are: Standardized residual; Studentized residual; Mahalanobis distance and leverage.

The function also generates plot(s) of the selected outlier detection measure(s) considering a deletion of each study included in the network (Shift the mean measures). Candidate statistics to be monitored are: 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 variance-covariance 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	measplot(object, stat, measure = "simple")

`object`	an object of class NMAoutlier.measures (mandatory).
`stat`	selected statistical outlier and influential detection measure (mandatory), For simply outlier and influential measures available choices are: ("estand"/ "estud"/ "mah"/ "leverage"). For outlier and influential deletion measures available choices are: ("estand.deleted", "estud.deleted", "leverage.leaveoneout", "weight.leaveoneout", "heterog.leaveoneout", "covratio", "cook", "rheterogeneity", "restimates", "rqhet", "rqinc", "rqtotal", "dfbetas")
`measure`	Outlier and influential detection measures. Simple measures (default: "simple") and measures considered study deletion (measure = "deletion").

Plot of outlier and influential (simple or/and deletion) detection measures for each study included in the network. Vertical axis provides each study included in the network (or the study deleted for outlier deletion measures). Horizontal axis provides a monitoring outlier and influential detection measure.

Maria Petropoulou <petropoulou@imbi.uni-freiburg.de>

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 each study in the
# network
measures <- NMAoutlier.measures(p1)

# plot of standardized residuals for each study
measplot(measures, "estand")

# plot of Mahalanobis distance values for each study
measplot(measures, "mah")

# plot of leverage values for each study
measplot(measures, "leverage")

## Not run: 
# Outlier detection measures considered deletion each time of an
# included study
deletion <- NMAoutlier.measures(p1, measure = "deletion")

# plot for R statistic for heterogeneity estimator
measplot(deletion, "rheterogeneity", measure = "deletion")

# plot for R statistic for Qinconsistency
measplot(deletion, "rqinc", measure = "deletion")

# plot of COVRATIO values when considering deletion for each study
measplot(deletion, "covratio", measure = "deletion")

## End(Not run)