multinma: add_integration – R documentation

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add_integration

Add numerical integration points to aggregate data

Description

The add_integration() generic creates numerical integration points using a Gaussian copula approach, as described in Phillippo et al. (2020). Methods are available for networks stored in nma_data objects, and for data frames. The function unnest_integration() unnests integration points stored in a data frame, to aid plotting or other exploration.

Usage

add_integration(x, ...)

## Default S3 method:
add_integration(x, ...)

## S3 method for class 'data.frame'
add_integration(x, ..., cor = NULL, n_int = 1000L, int_args = list())

## S3 method for class 'nma_data'
add_integration(x, ..., cor = NULL, n_int = 1000L, int_args = list())

unnest_integration(data)

Arguments

`x`	An `nma_data` object, as created by the `set_*()` functions or `combine_network()`, or data frame
`...`	Distributions for covariates, see "Details"
`cor`	Correlation matrix to use for generating the integration points. By default, this takes a weighted correlation matrix from all IPD studies. Rows and columns should match the order of covariates specified in `...`.
`n_int`	Number of integration points to generate, default 1000
`int_args`	A named list of arguments to pass to `sobol()`
`data`	Data frame with nested integration points, stored in list columns as `.int_<variable name>`

Details

The arguments passed to ... specify distributions for the covariates. Argument names specify the name of the covariate, which should match a covariate name in the IPD (if IPD are present). The required marginal distribution is then specified using the function distr().

Value

For the nma_data method, an object of class nma_data. For the data.frame method, the input data frame is returned (as a tibble) with an added column for each covariate (prefixed with ".int_"), containing the numerical integration points nested as length-n_int vectors within each row. For unnest_integration(), a data frame with integration points unnested.

References

Phillippo DM, Dias S, Ades AE, Belger M, Brnabic A, Schacht A, Saure D, Kadziola Z, Welton NJ (2020). “Multilevel Network Meta-Regression for population-adjusted treatment comparisons.” Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(3), 1189–1210. doi: 10.1111/rssa.12579, https://doi.org/10.1111/rssa.12579.

Examples

## Plaque psoriasis ML-NMR - network setup and adding integration points
# 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

# Add integration points to the network
pso_net <- add_integration(pso_net,
  durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
  prevsys = distr(qbern, prob = prevsys),
  bsa = distr(qlogitnorm, mean = bsa_mean, sd = bsa_sd),
  weight = distr(qgamma, mean = weight_mean, sd = weight_sd),
  psa = distr(qbern, prob = psa),
  n_int = 1000)


## Adding integration points to a data frame, e.g. for prediction
# Define a data frame of covariate summaries
new_agd_int <- data.frame(
  bsa_mean = 0.6,
  bsa_sd = 0.3,
  prevsys = 0.1,
  psa = 0.2,
  weight_mean = 10,
  weight_sd = 1,
  durnpso_mean = 3,
  durnpso_sd = 1)

# Adding integration points, using the weighted average correlation matrix
# computed for the plaque psoriasis network
new_agd_int <- add_integration(new_agd_int,
  durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
  prevsys = distr(qbern, prob = prevsys),
  bsa = distr(qlogitnorm, mean = bsa_mean, sd = bsa_sd),
  weight = distr(qgamma, mean = weight_mean, sd = weight_sd),
  psa = distr(qbern, prob = psa),
  cor = pso_net$int_cor,
  n_int = 1000)

new_agd_int

multinma

Bayesian Network Meta-Analysis of Individual and Aggregate Data

v0.3.0

GPL-3

Authors

David M. Phillippo [aut, cre] (<https://orcid.org/0000-0003-2672-7841>)

Initial release