netmeta (version 0.9-8)

netcomb: Additive network meta-analysis for combinations of treatments

Description

Some treatments in a network meta-analysis may be combinations of other treatments or have common components. The influence of individual components can be evaluated in an additive network meta-analysis model assuming that the effect of treatment combinations is the sum of the effects of its components. This function implements this additive model in a frequentist way.

Usage

netcomb(x, inactive = NULL, sep.components = "+", C.matrix,
        comb.fixed = x$comb.fixed,
        comb.random = x$comb.random | !is.null(tau.preset),
        tau.preset = NULL)

Arguments

x

An object of class netmeta.

inactive

A character string defining the inactive treatment (see Details).

sep.components

A single character to define separator between treatment components.

C.matrix

C matrix (see Details).

comb.fixed

A logical indicating whether a fixed effect meta-analysis should be conducted.

comb.random

A logical indicating whether a random effects meta-analysis should be conducted.

tau.preset

An optional value for the square-root of the between-study variance \(\tau^2\).

Value

A list containing the following components:

x, inactive, sep.components, C.matrix

As defined above.

comb.fixed, comb.random, tau.preset

As defined above.

k

Total number of studies.

m

Total number of pairwise comparisons.

n

Total number of treatments.

c

Total number of components.

comparisons.fixed, comparisons.random

Lists with components studlab, treat1, treat2, TE, seTE, lower, upper, z, p level, df (fixed and random effects model).

components.fixed, components.random

Lists with components TE, seTE, lower, upper, z, p level, df (fixed and random effects model).

combinations.fixed, combinations.random

Lists with components TE, seTE, lower, upper, z, p level, df (fixed and random effects model).

sm

Summary measure.

level.comb

Level for confidence intervals.

Q.additive

Overall heterogeneity / inconsistency statistic (additive model).

df.Q.additive

Degrees of freedom for test of heterogeneity / inconsistency (additive model).

pval.Q.additive

P-value for test of heterogeneity / inconsistency (additive model).

Q.standard

Overall heterogeneity / inconsistency statistic (standard model).

df.Q.standard

Degrees of freedom for test of heterogeneity / inconsistency (standard model).

pval.Q.standard

P-value for test of heterogeneity / inconsistency (standard model).

Q.diff.fixed, Q.diff.random

Test statistic for difference in goodness of fit between standard and additive model (fixed and random effects model).

df.Q.diff.fixed, df.Q.diff.random

Degrees of freedom for difference in goodness of fit between standard and additive model (fixed and random effects model).

pval.Q.diff.fixed, pval.Q.diff.random

P-value for difference in goodness of fit between standard and additive model (fixed and random effects model).

backtransf

A logical indicating whether results should be back transformed in printouts and forest plots.

nchar.trts

A numeric defining the minium number of characters used to create unique treatment and component names.

title

Title of meta-analysis / systematic review.

call

Function call.

version

Version of R package netmeta used to create object.

Details

Treatments in network meta-analysis (NMA) can be complex interventions. Some treatments may be combinations of others or have common components. The standard analysis provided by netmeta is a NMA where all existing (single or combined) treatments are considered as different nodes in the network. Exploiting the fact that some treatments are combinations of common components, an additive NMA model can be used to evaluate the influence of individual components. This model assumes that the effect of a treatment combination is the sum of the effects of its components which implies that common components cancel out in comparisons.

The additive network meta-analysis model has been implemented using Bayesian methods (Mills et al., 2012; Welton et al., 2013). This function implements the additive model in a frequentist way (R<U+00FC>cker & Schwarzer, 2017).

The underlying multivariate model is given by

$$\bold{\delta} = \bold{B} \bold{\theta}, \bold{\theta} = \bold{C} \bold{\beta}$$

with

• [\(\bold{\delta}\)] vector of true treatment effects (differences) from individual studies,

• [\(\bold{B}\)] is a design matrix describing the structure of the network,

• [\(\bold{\theta}\)] parameter vector that represents the existing combined treatments,

• [\(\bold{C}\)] matrix describing how the treatments are composed,

• [\(\bold{\beta}\)] is a parameter vector representing the treatment components.

All parameters are estimated using weighted least squares regression.

Argument inactive can be used to specify a single component that does not have any therapeutic value. Accordingly, it is assumed that the treatment effect of the combination of this component with an additional treatment component is equal to the treatment effect of the additional component alone.

Argument sep.components can be used to specify the separator between individual components. By default, the matrix C is calculated internally from treatment names. However, it is possible to specify a different matrix using argument C.matrix.

References

K<U+00F6>nig J, Krahn U, Binder H (2013). Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Statistics in Medicine, 32(30), 5414--29.

Mills EJ, Thorlund K, Ioannidis JP (2012). Calculating additive treatment effects from multiple randomized trials provides useful estimates of combination therapies. Journal of Clinical Epidemiology, 65(12), 1282--8. Doi: 10.1016/j.jclinepi.2012.07.012.

R<U+00FC>cker G, Schwarzer G (2017). Network meta-analysis of combinations of treatments. Manuscript.

Welton NJ, Caldwell DM, Adamopoulos E, Vedhara K (2009). Mixed treatment comparison meta-analysis of complex interventions: psychological interventions in coronary heart disease. American Journal of Epidemiology, 169(9): 1158--65. Doi: 10.1093/aje/kwp014.

See Also

netmeta, print.netcomb, discomb

Examples

# NOT RUN {
data(Linde2016)
#
# Specify, order of treatments
#
trts <- c("TCA", "SSRI", "SNRI", "NRI", "Low-dose SARI", "NaSSa",
          "rMAO-A", "Ind drug", "Hypericum", "Face-to-face CBT",
          "Face-to-face PST", "Face-to-face interpsy", "Face-to-face psychodyn",
          "Other face-to-face", "Remote CBT", "Self-help CBT", "No contact CBT",
          "Face-to-face CBT + SSRI", "Face-to-face interpsy + SSRI",
          "Face-to-face PST + SSRI", "UC", "Placebo")
#
# Note, three treatments are actually combinations of 'SSRI' with
# other components:
#
# "Face-to-face CBT + SSRI",
# "Face-to-face interpsy + SSRI",
# "Face-to-face PST + SSRI"

# Standard Network meta-analysis
#
net1 <- netmeta(lnOR, selnOR, treat1, treat2, id,
                data = Linde2016, ref = "placebo",
		seq = trts,
                sm = "OR", comb.fixed = FALSE, comb.random = TRUE)
#
summary(net1)

# Additive model for treatment components (with placebo as inactive
# treatment)
#
nc1 <- netcomb(net1, inactive = "placebo")
#
summary(nc1)
# }