netmeta (version 3.6-0)

netcomplex: Calculate effect of arbitrary complex interventions in component network meta-analysis

Description

Calculate effect of arbitrary complex interventions (i.e., combinations of several components) in component network meta-analysis.

Usage

netcomplex(
  x,
  complex,
  common = x$common,
  random = x$random,
  level = x$level.ma,
  nchar.comps = x$nchar.trts,
  backtransf = x$backtransf,
  warn.deprecated = gs("warn.deprecated"),
  ...
)
# S3 method for netcomplex
print(
  x,
  common = x$common,
  random = x$random,
  backtransf = x$backtransf,
  nchar.comps = x$nchar.comps,
  digits = gs("digits"),
  digits.stat = gs("digits.stat"),
  digits.pval = gs("digits.pval"),
  scientific.pval = gs("scientific.pval"),
  zero.pval = gs("zero.pval"),
  JAMA.pval = gs("JAMA.pval"),
  big.mark = gs("big.mark"),
  legend = gs("legend"),
  warn.deprecated = gs("warn.deprecated"),
  ...
)

Value

A list is returned by the function netcomplex with the following elements:

complex

Complex intervention(s).

Comb.common, Comb.random

A vector of combination effects (common and random effects model).

seComb.common, seComb.random

A vector with corresponding standard errors (common and random effects model).

lower.Comb.common, lower.Comb.random

A vector with lower confidence limits for combinations (common and random effects model).

upper.Comb.common, upper.Comb.random

A vector with upper confidence limits for combinations (common and random effects model).

statistic.Comb.common, statistic.Comb.random

A vector with z-values for the overall effect of combinations (common and random effects model).

pval.Comb.common, pval.Comb.random

A vector with p-values for the overall effect of combinations (common and random effects model).

common, random

A defined above.

level, nchar.comps, backtransf, x

A defined above.

C.matrix

C matrix.

Arguments

x

An object of class netcomb or netcomplex (print function).

complex

A matrix, vector or single numeric defining the complex intervention(s) (see Details).

common

A logical indicating whether results for common effects model should be conducted.

random

A logical indicating whether results for random effects model should be conducted.

level

The level used to calculate confidence intervals for combinations of components.

nchar.comps

A numeric defining the minimum number of characters used to create unique names for components (see Details).

backtransf

A logical indicating whether printed results should be back transformed. If backtransf=TRUE, results for sm="OR" are printed as odds ratios rather than log odds ratios.

warn.deprecated

A logical indicating whether warnings should be printed if deprecated arguments are used.

...

Additional arguments (to catch deprecated arguments).

digits

Minimal number of significant digits, see print.default.

digits.stat

Minimal number of significant digits for z-value of test for overall effect, see print.default.

digits.pval

Minimal number of significant digits for p-values, see print.default.

scientific.pval

A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345.

zero.pval

A logical specifying whether p-values should be printed with a leading zero.

JAMA.pval

A logical specifying whether p-values for test of combination effect should be printed according to JAMA reporting standards.

big.mark

A character used as thousands separator.

legend

A logical indicating whether a legend should be printed.

Author

Guido Schwarzer

Details

R functions netcomb and discomb only report results for complex interventions present in the network. This function can be used to calculate the effect for arbitrary complex interventions.

Complex interventions can be specified using argument complex:

• a character vector with definition of complex interventions,

• a single numeric defining the number of components to combine in a complex intervention,

• a dedicated C matrix.

In order to calculate effects of arbitrary complex interventions, a C matrix is needed which describes how the complex interventions are composed by the components (Rücker et al., 2020, Section 3.2). The C matrix is constructed internally if not provided by argument complex. All complex interventions occuring in the network are considered if argument complex is missing.

By default, component names are not abbreviated in printouts. However, in order to get more concise printouts, argument nchar.comps can be used to define the minimum number of characters for abbreviated component names (see abbreviate, argument minlength). R function treats is utilised internally to create abbreviated component names.

References

Rücker G, Petropoulou M, Schwarzer G (2020): Network meta-analysis of multicomponent interventions. Biometrical Journal, 62, 808--21

See Also

netcomb, discomb, netcomparison

Examples

# Only consider studies including Face-to-face PST (to reduce
# runtime of example)
#
face <- subset(Linde2016, id %in% c(16, 24, 49, 118))

# Use pairwise() to transform data to comparison-based format
#
pw <- pairwise(treat = int,
  event = resp, n = n, studlab = paste(author, year),
  data = face, reference = "plac", sm = "OR")

# Conduct random effects network meta-analysis
#
nma1 <- netmeta(pw, common = FALSE)

# Additive model for treatment components (with placebo as inactive
# treatment)
#
cnma1 <- netcomb(nma1, inactive = "placebo")

# Result for combination Face-to-face PST + SSRI
netcomplex(cnma1, "Face-to-face PST + SSRI", nchar.comps = 4)
netcomplex(cnma1, "F + S", nchar.comps = 4)

# Result for combination Face-to-face PST + SSRI + Placebo
netcomplex(cnma1, "Face-to-face PST + SSRI + Plac", nchar.comps = 4)

# \donttest{
# Artificial example
t1 <- rep("A", 3)
t2 <- c("B+C", "A+C", "C+D")
TE <- c(0, 1, 0)
seTE <- rep(1, 3)
# Conduct (C)NMA
nma2 <- netmeta(TE, seTE, t1, t2, random = FALSE)
cnma2 <- netcomb(nma2)

# Result for combination A + B + C
netcomplex(cnma2, "A + B + C")
# Same results
netcomplex(cnma2, "A+B+C")
netcomplex(cnma2, "B+C+A")
netcomplex(cnma2, "C+B+A")
netcomplex(cnma2, "c+b+a")

# Generated C matrix
netcomplex(cnma2, c(LETTERS[1:4], "A+B+C"))$C.matrix

# Results for all possible combinations of two components
netcomplex(cnma2, 2)
# Results for all possible combinations of three components
netcomplex(cnma2, 3)
# }