NMAoutlier (version 0.2.0)

fwdplot: Forward plot(s) to monitor selected statistic(s)/method(s)

Description

This function generates forward plot(s) to monitor selected statistic(s) and/or method(s). The function creates a plot of the selected monitoring measure throughout the iterations of the Forward Search algorithm. Candidate statistics to be monitored can be: P-score; z-values by back-calculation method to derive indirect estimates from direct pairwise comparisons and network estimates; standardized residuals; heterogeneity variance estimator; Cook's distance; ratio of variances; Q statistics (Overall heterogeneity / inconsistency Q statistic (Q), overall heterogeneity Q statistic (Q), between-designs Q statistic (Q), based on a random effects design-by-treatment interaction model).

Usage

fwdplot(x, stat, select.st = NULL)

Arguments

x

an object of class NMAoutlier (mandatory).

stat

statistical measure to be monitored in forward plot(s) (mandatory), available choices are: "pscore", "nsplit", "estand", "heterog", "cook", "ratio", or "Q" (can be abbreviated).

select.st

selected statistic (pscore/nsplit/estand) for selected treatment(s)/comparison(s)/study

Author

Maria Petropoulou <[e-mail]>

Details

Plot of statistical measures for each iteration of search. Vertical axis provides the FS iterations. Horizontal axis provides the values of the monitoring statistical measure.

Examples

if (FALSE) {
library("netmeta")
data(smokingcessation)
smokingcessation$id <- 1:nrow(smokingcessation)

study912 <- subset(smokingcessation, id %in% 9:12)
p1 <- pairwise(list(treat1, treat2, treat3),
  list(event1, event2, event3), list(n1, n2, n3),
  data = study912, sm = "OR")

# Forward search algorithm
#
FSresult <- NMAoutlier(p1, P = 1, small.values = "bad", n_cores = 2)

# forward plot for Cook's distance
fwdplot(FSresult, "cook")

data(smokingcessation)

# Transform data from arm-based to contrast-based format
# Use 'sm' argument for odds ratios.
# Use function pairwise from netmeta package

p1 <- pairwise(list(treat1, treat2, treat3),
  list(event1, event2, event3), list(n1, n2, n3),
  data = smokingcessation, sm = "OR")

# Forward Search algorithm
FSresult <- NMAoutlier(p1, small.values = "bad")
FSresult

# forward plot for Cook's distance
fwdplot(FSresult, "cook")

# forward plot for ratio of variances
fwdplot(FSresult, "ratio")

# forward plot for heterogeneity estimator
fwdplot(FSresult, "heterog")

# forward plot for Q statistics
fwdplot(FSresult, "Q")

# forward plot for P-scores
fwdplot(FSresult, "pscore")

# forward plot monitoring P-scores for treatment A
fwdplot(FSresult,"pscore", "A")

# forward plot for z-values of disagreement of direct and indirect evidence
fwdplot(FSresult, "nsplit")

# forward plot for z-values of disagreement of direct and indirect evidence
# monitoring treatment comparison A versus B
fwdplot(FSresult, "nsplit", "A:B")

# forward plot for standardized residual for study 4
fwdplot(FSresult, "estand", 4)
}