nmarank (version 0.3-0)

condition: 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, ...)

Value

A list with the defined function and its arguments.

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))

See Also

nmarank

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))