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Set up contrast-based aggregate data

Source: R/nma_data.R
set_agd_contrast.Rd

Set up a network containing contrast-based aggregate data (AgD), i.e. summaries of relative effects between treatments such as log Odds Ratios. Multiple data sources may be combined once created using combine_network().

Usage

set_agd_contrast(
  data,
  study,
  trt,
  y = NULL,
  se = NULL,
  sample_size = NULL,
  trt_ref = NULL,
  trt_class = NULL
)

Arguments

data

a data frame

study

column of data specifying the studies, coded using integers, strings, or factors

trt

column of data specifying treatments, coded using integers, strings, or factors

y

column of data specifying a continuous outcome

se

column of data specifying the standard error for a continuous outcome

sample_size

column of data giving the sample size in each arm. Optional, see details.

trt_ref

reference treatment for the network, as a single integer, string, or factor. If not specified, a reasonable well-connected default will be chosen (see details).

trt_class

column of data specifying treatment classes, coded using integers, strings, or factors. By default, no classes are specified.

Value

An object of class nma_data

Details

Each study should have a single reference/baseline treatment, against which relative effects in the other arm(s) are given. For the reference arm, include a data row with continuous outcome y equal to NA. If a study has three or more arms (so two or more relative effects), set the standard error se for the reference arm data row equal to the standard error of the mean outcome on the reference arm (this determines the covariance of the relative effects, when expressed as differences in mean outcomes between arms).

All arguments specifying columns of data accept the following:

  • A column name as a character string, e.g. study = "studyc"

  • A bare column name, e.g. study = studyc

  • dplyr::mutate() style semantics for inline variable transformations, e.g. study = paste(author, year)

By default, trt_ref = NULL and a network reference treatment will be chosen that attempts to maximise computational efficiency and stability. If an alternative reference treatment is chosen and the model runs slowly or has low effective sample size (ESS) this may be the cause - try letting the default reference treatment be used instead. Regardless of which treatment is used as the network reference at the model fitting stage, results can be transformed afterwards: see the trt_ref argument of relative_effects() and predict.stan_nma().

The sample_size argument is optional, but when specified:

  • Enables automatic centering of predictors (center = TRUE) in nma() when a regression model is given for a network combining IPD and AgD

  • Enables production of study-specific relative effects, rank probabilities, etc. for studies in the network when a regression model is given

  • Nodes in plot.nma_data() may be weighted by sample size

See also

set_ipd() for individual patient data, set_agd_arm() for arm-based aggregate data, and combine_network() for combining several data sources in one network.

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

Examples

# Set up network of Parkinson's contrast data
head(parkinsons)
#>   studyn trtn     y    se   n  diff se_diff
#> 1      1    1 -1.22 0.504  54    NA   0.504
#> 2      1    3 -1.53 0.439  95 -0.31   0.668
#> 3      2    1 -0.70 0.282 172    NA   0.282
#> 4      2    2 -2.40 0.258 173 -1.70   0.382
#> 5      3    1 -0.30 0.505  76    NA   0.505
#> 6      3    2 -2.60 0.510  71 -2.30   0.718

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

# Print details
park_net
#> A network with 7 AgD studies (contrast-based).
#> 
#> -------------------------------------------------- AgD studies (contrast-based) ---- 
#>  Study Treatment arms
#>  1     2: 1 | 3      
#>  2     2: 1 | 2      
#>  3     3: 4 | 1 | 2  
#>  4     2: 4 | 3      
#>  5     2: 4 | 3      
#>  6     2: 4 | 5      
#>  7     2: 4 | 5      
#> 
#>  Outcome type: continuous
#> ------------------------------------------------------------------------------------
#> Total number of treatments: 5
#> Total number of studies: 7
#> Reference treatment is: 4
#> Network is connected

# Plot network
plot(park_net)