multinma (version 0.6.1)

set_ipd: Set up individual patient data

Description

Set up a network containing individual patient data (IPD). Multiple data sources may be combined once created using combine_network().

Usage

set_ipd(
  data,
  study,
  trt,
  y = NULL,
  r = NULL,
  E = NULL,
  Surv = NULL,
  trt_ref = NULL,
  trt_class = NULL
)

Value

An object of class nma_data

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

r

column of data specifying a binary outcome or Poisson outcome count

E

column of data specifying the total time at risk for Poisson outcomes

Surv

column of data specifying a survival or time-to-event outcome, using the Surv() function. Right/left/interval censoring and left truncation (delayed entry) are supported.

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.

Details

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().

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)

See Also

set_agd_arm() for arm-based aggregate data, set_agd_contrast() for contrast-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 plaque psoriasis IPD
head(plaque_psoriasis_ipd)

pso_net <- set_ipd(plaque_psoriasis_ipd,
                   study = studyc,
                   trt = trtc,
                   r = pasi75)

# Print network details
pso_net

# Plot network
plot(pso_net)

# Setting a different reference treatment
set_ipd(plaque_psoriasis_ipd,
        study = studyc,
        trt = trtc,
        r = pasi75,
        trt_ref = "PBO")