R: End-user-ready results for unrelated trial effects model

unrelated_effects_plot {rnmamod}

R Documentation

End-user-ready results for unrelated trial effects model

Description

Performs the unrelated trial effects model (also known as fixed effects model) and illustrates the results of each trial and corresponding pairwise comparison.

Usage

unrelated_effects_plot(
  data,
  measure,
  char,
  drug_names,
  trial_names,
  mean_misspar,
  var_misspar,
  rho,
  save_xls
)

Arguments

`data`	A data-frame of a one-trial-per-row format containing arm-level data of each trial. See 'Format' in `run_model`.
`measure`	Character string indicating the effect measure with values `"OR"`, `"MD"`, `"SMD"`, or `"ROM"` for the odds ratio, mean difference, standardised mean difference and ratio of means, respectively.
`char`	A data-frame of three columns and number of rows equal to the number of trials in `data`. Each column refers to a trial-characteristic with nominal elements.
`drug_names`	A vector of labels with the name of the interventions in the order they appear in the argument `data`. If `drug_names` is not defined, the order of the interventions as they appear in `data` is used, instead.
`trial_names`	A vector of labels with the name of the trials in the order they appear in the argument `data`. If `trial_names` is not defined, the order of the trials as they appear in `data` is used, instead.
`mean_misspar`	A numeric value for the mean of the normal distribution of the informative missingness parameter (see 'Details'). The default argument is 0 and corresponds to the missing-at-random assumption. The same value is considered across all trials of the dataset.
`var_misspar`	A positive non-zero number for the variance of the normal distribution of the informative missingness parameter. When the `measure` is `"OR"`, `"MD"`, or `"SMD"` the default argument is 1. When the `measure` is `"ROM"` the default argument is 0.04. The same value is considered across all trials of the dataset.
`rho`	A numeric value in the interval [-1, 1] that indicates the correlation coefficient between two informative missingness parameters in a trial. The same value is considered across all trials of the dataset. The default argument is 0 and corresponds to uncorrelated missingness parameters.
`save_xls`	Logical to indicate whether to export the tabulated results to an 'xlsx' file (via the `write_xlsx` function of the R-package writexl) to the working directory of the user. The default is `FALSE` (do not export).

Details

The unrelated trial effects model may be an alternative to network meta-analysis, when the latter is not deemed appropriate (e.g., there is considerable statistical heterogeneity, or substantial intransitivity). In the presence of missing participant outcome data, the effect size and standard error are adjusted by applying the pattern-mixture model with Taylor series in trial-arms with reported missing participants (Mavridis et al., 2015; White et al., 2008). The unrelated_effects_plot function calls the taylor_imor and taylor_continuous functions (for a binary and continuous outcome, respectively) to employ pattern-mixture model with Taylor series. The unrelated_effects_plot function considers the informative missingness odds ratio in the logarithmic scale for binary outcome data (White et al., 2008), the informative missingness difference of means when measure is "MD" or "SMD", and the informative missingness ratio of means in the logarithmic scale when measure is "ROM" (Mavridis et al., 2015).

The number of interval plots equals the number of observed comparisons in the network. In each interval plot, the y-axis refers to all trials of the network and x-axis refers to the selected effect measure. The odds ratio and ratio of means are calculated in the logarithmic scale but they are reported in their original scale after exponentiation.

unrelated_effects_plot depicts all three characteristics for each trial using different colours, line-types and point-shapes for the corresponding 95% confidence interval and point estimate. Ideally, each characteristic should have no more than three categories; otherwise, the plot becomes cluttered. For now, the unrelated_effects_plot function uses the default colour palette, line-types and point-shapes.

Value

A panel of interval plots for each observed comparison in the network, when there are up to 15 trials in the data. Otherwise, unrelated_effects_plot exports a data-frame to an 'xlsx' file at the working directory of the user. This data-frame includes the data in the long format, the within-trial effect measure and 95% confidence interval of the corresponding comparisons, the interventions compared, and the three characteristics (as defined in char). For datasets with more than 15 trials, the plot becomes cluttered and it is difficult to identify the trial-names. Hence, exporting the results in an Excel file is a viable alternative.

Author(s)

Loukia M. Spineli

References

Mavridis D, White IR, Higgins JP, Cipriani A, Salanti G. Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis. Stat Med 2015;34(5):721–41. doi: 10.1002/sim.6365

White IR, Higgins JP, Wood AM. Allowing for uncertainty due to missing data in meta-analysis–part 1: two-stage methods. Stat Med 2008;27(5):711–27. doi: 10.1002/sim.3008