combine_network {multinma}

Combine multiple data sources into one network

Description

Multiple data sources created using set_ipd(), set_agd_arm(), or set_agd_contrast() can be combined into a single network for analysis.

Usage

combine_network(..., trt_ref)

Arguments

...

multiple data sources, as defined using the ⁠set_*⁠ functions

trt_ref

reference treatment for the entire network, as a string (or coerced as such) referring to the levels of the treatment factor variable

Value

An object of class nma_data

See Also

set_ipd(), set_agd_arm(), and set_agd_contrast() for defining different data sources.

print.nma_data() for the print method displaying details of the network, and plot.nma_data() for network plots.

Examples

## Parkinson's - combining contrast- and arm-based data
studies <- parkinsons$studyn
(parkinsons_arm <- parkinsons[studies %in% 1:3, ])
(parkinsons_contr <- parkinsons[studies %in% 4:7, ])

park_arm_net <- set_agd_arm(parkinsons_arm,
                            study = studyn,
                            trt = trtn,
                            y = y,
                            se = se,
                            sample_size = n)

park_contr_net <- set_agd_contrast(parkinsons_contr,
                                   study = studyn,
                                   trt = trtn,
                                   y = diff,
                                   se = se_diff,
                                   sample_size = n)

park_net <- combine_network(park_arm_net, park_contr_net)

# Print network details
park_net

# Plot network
plot(park_net, weight_edges = TRUE, weight_nodes = TRUE)

## Plaque Psoriasis - combining IPD and AgD in a network
# Set up plaque psoriasis network combining IPD and AgD
library(dplyr)
pso_ipd <- filter(plaque_psoriasis_ipd,
                  studyc %in% c("UNCOVER-1", "UNCOVER-2", "UNCOVER-3"))

pso_agd <- filter(plaque_psoriasis_agd,
                  studyc == "FIXTURE")

head(pso_ipd)
head(pso_agd)

pso_ipd <- pso_ipd %>%
  mutate(# Variable transformations
    bsa = bsa / 100,
    prevsys = as.numeric(prevsys),
    psa = as.numeric(psa),
    weight = weight / 10,
    durnpso = durnpso / 10,
    # Treatment classes
    trtclass = case_when(trtn == 1 ~ "Placebo",
                         trtn %in% c(2, 3, 5, 6) ~ "IL blocker",
                         trtn == 4 ~ "TNFa blocker"),
    # Check complete cases for covariates of interest
    complete = complete.cases(durnpso, prevsys, bsa, weight, psa)
  )

pso_agd <- pso_agd %>%
  mutate(
    # Variable transformations
    bsa_mean = bsa_mean / 100,
    bsa_sd = bsa_sd / 100,
    prevsys = prevsys / 100,
    psa = psa / 100,
    weight_mean = weight_mean / 10,
    weight_sd = weight_sd / 10,
    durnpso_mean = durnpso_mean / 10,
    durnpso_sd = durnpso_sd / 10,
    # Treatment classes
    trtclass = case_when(trtn == 1 ~ "Placebo",
                         trtn %in% c(2, 3, 5, 6) ~ "IL blocker",
                         trtn == 4 ~ "TNFa blocker")
  )

# Exclude small number of individuals with missing covariates
pso_ipd <- filter(pso_ipd, complete)

pso_net <- combine_network(
  set_ipd(pso_ipd,
          study = studyc,
          trt = trtc,
          r = pasi75,
          trt_class = trtclass),
  set_agd_arm(pso_agd,
              study = studyc,
              trt = trtc,
              r = pasi75_r,
              n = pasi75_n,
              trt_class = trtclass)
)

# Print network details
pso_net


# Plot network
plot(pso_net, weight_nodes = TRUE, weight_edges = TRUE, show_trt_class = TRUE)