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End-user-ready results for network meta-regression

Source: R/metareg.plot_function.R
metareg_plot.Rd

Illustrates the effect estimates, predictions and regression coefficients of comparisons with a specified comparator intervention for a selected covariate value and also exports these results to an Excel file.

Usage

metareg_plot(
  full,
  reg,
  compar,
  cov_name = "covariate value",
  drug_names,
  save_xls
)

Arguments

full

An object of S3 class run_model. See 'Value' in run_model.

reg

An object of S3 class run_metareg. See 'Value' in run_metareg.

compar

A character to indicate the comparator intervention. It must be any name found in drug_names.

cov_name

A character or text to indicate the name of the covariate.

drug_names

A vector of labels with the name of the interventions in the order they appear in the argument data of run_model. If drug_names is not defined, the order of the interventions as they appear in data is used, instead.

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) at the working directory of the user. The default is FALSE (do not export).

Value

metareg_plot prints on the R console a message on the most parsimonious model (if any) based on the DIC (in red text). Furthermore, the function returns the following list of elements:

table_estimates

The posterior median, and 95% credible interval of the summary effect measure (according to the argument measure defined in run_model) for each comparison with the selected intervention under network meta-analysis and network meta-regression based on the specified cov_value.

table_predictions

The posterior median, and 95% prediction interval of the summary effect measure (according to the argument measure defined in run_model) for each comparison with the selected intervention under network meta-analysis and network meta-regression based on the covariate value specified in run_metareg.

table_model_assessment

The DIC, total residual deviance, number of effective parameters, and the posterior median and 95% credible interval of between-trial standard deviation (tau) under each model (Spiegelhalter et al., 2002). When a fixed-effect model has been performed, metareg_plot does not return results on tau. For a binary outcome, the results refer to the odds ratio scale.

table_regression_coeffients

The posterior median and 95% credible interval of the regression coefficient(s) (according to the argument covar_assumption defined in run_metareg). For a binary outcome, the results refer to the odds ratio scale.

interval_plot

A forest plot on the estimated and predicted effect sizes of comparisons with the selected comparator intervention under network meta-analysis and network meta-regression based on the covariate value specified in run_metareg alongside a forest plot with the corresponding SUCRA values. See 'Details' and 'Value' in forestplot_metareg.

sucra_scatterplot

A scatterplot of the SUCRA values from the network meta-analysis against the SUCRA values from the network meta-regression based on the covariate value specified in run_metareg. See 'Details' and 'Value' in scatterplot_sucra.

Details

The deviance information criterion (DIC) of the network meta-analysis model is compared with the DIC of the network meta-regression model. If the difference in DIC exceeds 5, the network meta-regression model is preferred; if the difference in DIC is less than -5, the network meta-analysis model is preferred; otherwise, there is little to choose between the compared models.

Furthermore, metareg_plot exports all tabulated results to separate 'xlsx' files (via the write_xlsx function of the R-package writexl) to the working directory of the user.

metareg_plot can be used only for a network of interventions. In the case of two interventions, the execution of the function will be stopped and an error message will be printed on the R console.

References

Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 2011;64(2):163–71. doi: 10.1016/j.jclinepi.2010.03.016

Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc B 2002;64(4):583–616. doi: 10.1111/1467-9868.00353

See also

forestplot_metareg, run_metareg, run_model, scatterplot_sucra, write_xlsx

Author

Loukia M. Spineli

Examples

data("nma.baker2009")

# \donttest{
# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_baker.rds', package = 'rnmamod'))

# Read results from 'run_metareg' (exchangeable structure)
reg <- readRDS(system.file('extdata/reg_baker.rds', package = 'rnmamod'))

# Publication year as the covariate
pub_year <- c(1996, 1998, 1999, 2000, 2000, 2001, rep(2002, 5), 2003, 2003,
              rep(2005, 4), 2006, 2006, 2007, 2007)

# The names of the interventions in the order they appear in the dataset
interv_names <- c("placebo", "budesonide", "budesonide plus formoterol",
                  "fluticasone", "fluticasone plus salmeterol",
                  "formoterol", "salmeterol", "tiotropium")

# Plot the results from both models for all comparisons with salmeterol and
# average publication year
metareg_plot(full = res,
             reg = reg,
             compar = "salmeterol",
             cov_name = "mean publication year",
             drug_names = interv_names)
#> There is little to choose between the two models
#> $table_estimates
#> 
#> 
#> Table: Estimation for comparisons with salmeterol for mean publication year
#> 
#> |versus salmeterol           | Median NMA |  95% CrI NMA  | Median NMR | 95% CrI NMR  |
#> |:---------------------------|:----------:|:-------------:|:----------:|:------------:|
#> |budesonide                  |    0.57    | (0.25, 1.47)  |    0.55    | (0.23, 1.4)  |
#> |budesonide plus formoterol  |    0.73    | (0.33, 1.75)  |    0.71    | (0.32, 1.72) |
#> |tiotropium                  |    0.95    | (0.66, 1.34)  |    0.94    | (0.64, 1.32) |
#> |fluticasone plus salmeterol |    1.01    |  (0.56, 1.9)  |    1.04    |  (0.56, 2)   |
#> |fluticasone                 |    1.17    | (0.67, 2.19)  |    1.38    | (0.71, 2.86) |
#> |formoterol                  |    1.44    | (0.83, 2.45)  |    1.31    | (0.73, 2.3)  |
#> |placebo                     |    1.58    | (1.13, 2.27)* |    1.54    | (1.1, 2.21)* |
#> 
#> $table_predictions
#> 
#> 
#> Table: Prediction for comparisons with salmeterol for mean publication year
#> 
#> |versus salmeterol           | Median NMA | 95% CrI NMA  | Median NMR | 95% CrI NMR  |
#> |:---------------------------|:----------:|:------------:|:----------:|:------------:|
#> |budesonide                  |    0.57    | (0.24, 1.56) |    0.55    | (0.22, 1.5)  |
#> |budesonide plus formoterol  |    0.73    | (0.31, 1.88) |    0.71    | (0.3, 1.85)  |
#> |tiotropium                  |    0.96    | (0.56, 1.53) |    0.94    | (0.55, 1.54) |
#> |fluticasone plus salmeterol |    1.01    | (0.52, 2.06) |    1.04    | (0.51, 2.19) |
#> |fluticasone                 |    1.17    | (0.62, 2.41) |    1.38    | (0.66, 3.14) |
#> |formoterol                  |    1.43    | (0.75, 2.71) |    1.31    | (0.66, 2.55) |
#> |placebo                     |    1.57    |  (1, 2.68)   |    1.53    | (0.96, 2.64) |
#> 
#> $table_model_assessment
#> 
#> 
#> Table: Model assessment and between-trial standard deviation
#> 
#> |    |Analysis              |  DIC  |  pD   | Mean deviance | data points | Median tau | SD tau |95% CrI tau  |
#> |:---|:---------------------|:-----:|:-----:|:-------------:|:-----------:|:----------:|:------:|:------------|
#> |    |Network meta-analysis | 89.16 | 34.97 |     54.19     |     50      |    0.14    |  0.09  |(0.01, 0.35) |
#> |50% |Meta-regression       | 90.65 | 36.98 |     53.68     |     50      |    0.14    |  0.1   |(0.01, 0.36) |
#> 
#> $table_regression_coeffients
#> 
#> 
#> Table: Estimation of regression coefficient(s)
#> 
#> |versus salmeterol           | Median beta|95% CrI beta |
#> |:---------------------------|-----------:|:------------|
#> |budesonide                  |        1.00|(0.72, 1.4)  |
#> |budesonide plus formoterol  |        1.00|(0.72, 1.39) |
#> |tiotropium                  |        0.98|(0.86, 1.07) |
#> |fluticasone plus salmeterol |        0.97|(0.75, 1.11) |
#> |fluticasone                 |        1.01|(0.9, 1.21)  |
#> |formoterol                  |        1.03|(0.91, 1.34) |
#> |placebo                     |        0.96|(0.88, 1.05) |
#> 
#> $interval_plot

#> 
#> $sucra_scatterplot

#> 
# }