Direct and indirect evidence

Determine whether two treatments in a network are connected by direct and/or indirect evidence, and generate a list of comparisons with both direct and indirect evidence (i.e. potential inconsistency) for node-splitting.

Usage

get_nodesplits(network, include_consistency = FALSE)

has_direct(network, trt1, trt2)

has_indirect(network, trt1, trt2)

Arguments

network

An nma_data object, as created by the functions set_*() or combine_network().

include_consistency

Logical, whether to include a row of NAs to indicate that a consistency model (i.e. a model with no node-splitting) should also be fitted by the nma() function. Default is FALSE when calling get_nodesplits() by hand, and nma() sets this to TRUE by default.

trt1, trt2

Treatments, each as a single integer, string, or factor

Value

For has_direct() and has_indirect(), a single logical value. For get_nodesplits(), a data frame with two columns giving the comparisons for node-splitting.

Details

The list of comparisons for node-splitting is generated following the algorithm of van Valkenhoef et al. (2016) . A comparison between two treatments has the potential for inconsistency, and is thus considered for node-splitting, if the comparison has both direct evidence and independent indirect evidence.

The notion of independent indirect evidence is necessary when multi-arm trials are present, since by design these trials are internally consistent. A comparison between two treatments has independent indirect evidence if, after removing all studies comparing the two treatments from the network, the two treatments are still connected by a path of evidence. This is the criterion considered by the has_indirect() function.

References

van Valkenhoef G, Dias S, Ades AE, Welton NJ (2016). “Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis.” Research Synthesis Methods, 7(1), 80--93. doi:10.1002/jrsm.1167 .

Examples

# Parkinsons example
park_net <- set_agd_arm(parkinsons,
                        study = studyn,
                        trt = trtn,
                        y = y,
                        se = se,
                        trt_ref = 1)
#> Note: Optional argument `sample_size` not provided, some features may not be available (see ?set_agd_arm).

# View the network plot
plot(park_net)


# The 4 vs. 5 comparison is a spur on the network
has_direct(park_net, 4, 5)
#> [1] TRUE
has_indirect(park_net, 4, 5)
#> [1] FALSE

# 1 and 5 are not directly connected
has_direct(park_net, 1, 5)
#> [1] FALSE
has_indirect(park_net, 1, 5)
#> [1] TRUE

# The 1 vs. 2 comparison does not have independent indirect evidence, since
# the 1-2-4 loop is a multi-arm study
has_indirect(park_net, 1, 2)
#> [1] FALSE

# Get a list of comparisons with potential inconsistency for node-splitting
get_nodesplits(park_net)
#> # A tibble: 4 × 2
#>   trt1  trt2 
#>   <fct> <fct>
#> 1 1     3    
#> 2 1     4    
#> 3 2     4    
#> 4 3     4    

# See van Valkenhoef (2016) for a discussion of this example