condition {nmarank} | R Documentation |
Define which hierarchies to select
Description
Defines a condition that is of interest to be satisfied involving a set of treatments in the network.
Usage
condition(fn, ...)
Arguments
fn |
Character string specifiying type of condition. |
... |
Function arguments. |
Details
The following types of conditions are available.
The condition fn = "sameHierarchy"
checks whether a specific
hierarchy occurs. One additional unnamed argument has to be
provided in '...': a vector with a permutation of all treatment
names in the network.
The condition fn = "specificPosition"
checks whether a
treatment ranks in a specific position. Two additional unnamed
arguments have to be provided in '...': (1) name of the treatment
of interest and (2) a single numeric specifying the rank position.
The condition fn = "betterEqual"
checks whether a treatment
has a position better or equal to a specific rank. Two additional
unnamed arguments have to be provided in '...': (1) name of the
treatment of interest and (2) a single numeric specifying the rank
position.
The condition fn = "retainOrder"
checks whether a specific
order of two or more treatments is retained anywhere in the
hierarchy. One additional unnamed argument has to be provided in
'...': a vector with two or more treatment names providing the
order of treatments.
The condition fn = "biggerCIV"
checks whether the effect of
a treatment is bigger than that of a second treatment by more than
a given clinically important value (CIV) on an additive scale
(e.g. log odds ratio, log risk ratio, mean difference). Three
additional unnamed arguments have to be provided in '...': (1)
name of the first treatment, (2) name of the second treatment and
(3) a numerical value for the CIV. Note that the actual value of
the relative effect is considered independently of whether
small.values
is "desirable"
or "undesirable"
.
Composition of conditions for more complex queries:
Conditions can be combined to express more complex decision
trees. This can be done by using the special operators %AND%,
%OR%, %XOR% and the opposite
function. The combination
should be defined as a binary tree with the use of parentheses. If
A, B, C and D are conditions, we can for example combine them into
a complex condition E:
E = A %AND% (B %OR% (opposite(C) %XOR% D))
Value
A list with the defined function and its arguments.
See Also
Examples
data("Woods2010", package = "netmeta")
p1 <- pairwise(treatment, event = r, n = N, studlab = author,
data = Woods2010, sm = "OR")
net1 <- netmeta(p1, small.values = "good")
# criterionA if all treatments are in the exact defined order
criterionA <-
condition("sameHierarchy",
c("SFC", "Salmeterol", "Fluticasone", "Placebo"))
# criterionB respects the relative order of two or more treatments
criterionB <-
condition("retainOrder",
c("SFC", "Fluticasone", "Placebo"))
# Below we define the condition that SFC and Salmeterol are in the
# first two positions.
# We first define conditions that each one of them is in position 1
# or 2
criterionC1 <- condition("betterEqual", "SFC", 2)
criterionC2 <- condition("betterEqual", "Salmeterol", 2)
# We then combine them with operator %AND%
criterionC <- criterionC1 %AND% criterionC2
# Next we can feed the condition into nmarank to get the
# probability of the selection
nmarank(net1, criterionC,
text.condition =
"SFC and Salmeterol are the two best options", nsim = 100)
# We can further combine criteria
criterionD <- criterionA %AND% (criterionB %OR% opposite(criterionC))