improved_ume {rnmamod}

Detect the frail comparisons in multi-arm trials

Description

Detects the frail comparisons in multi-arm trials, that is, comparisons between non-baseline interventions not investigated in any two-arm trial in the network (Spineli, 2021). The 'original' model of Dias et al. (2013) omits the frail comparisons from the estimation process of the unrelated mean effects model. Consequently, their posterior distribution coincides with the prior distribution yielding implausible posterior standard deviations.

Usage

improved_ume(t, N, ns, na)

Arguments

t

A data-frame of the one-trial-per-row format containing the intervention identifier in each arm of every trial (see 'Details' below, and 'Format' in run_model).

N

A data-frame of the one-trial-per-row format containing the number of participants randomised to the assigned intervention in each arm of every trial (see 'Details' below, and 'Format' in run_model).

ns

A scale parameter on the number trials.

na

A vector of length equal to ns with the number of arms in each trial.

Details

improved_ume is integrated in run_ume and calls the output of data_preparation after sorting the rows so that multi-arm trials appear at the bottom of the dataset. When there are no multi-arm trials or no frail comparisons in the network, improved_ume returns only the element obs_comp (see, 'Value').

Value

The output of improved_ume is a list of elements that are inherited by run_ume:

nbase_multi

A scalar parameter on the number of frail comparisons.

t1_bn

A vector with numeric values referring to the first arm of each frail comparison.

t2_bn

A vector with numeric values referring to the second arm of each frail comparison.

ref_base

A scalar referring to the reference intervention for the subnetwork of interventions in frail comparisons.

base

A vector with numeric values referring to the baseline intervention of the multi-arm trials that contain the frail comparisons.

obs_comp

A data-frame that indicates how many two-arm and multi-arm trials have included each pairwise comparison observed in the network.

Author(s)

Loukia M. Spineli

References

Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades AE. Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making 2013;33(5):641–56. doi: 10.1177/0272989X12455847

Spineli LM. A revised framework to evaluate the consistency assumption globally in a network of interventions. Med Decis Making 2021. doi: 10.1177/0272989X211068005

See Also

data_preparation, run_model, run_ume