Description Usage Arguments Details Author(s) Examples

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).

1 |

`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 |

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.

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

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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | ```
## Not run:
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")
# 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, package = "netmeta")
# Transform data from arm-based to contrast-based format
# Use 'sm' argument for odds ratios.
# 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")
# 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)
## End(Not run)
``` |

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