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rnmamod: Bayesian Network Meta-analysis with Missing Participants
Description
An R package for performing Bayesian network meta-analysis while handling
missing participant outcome data properly, assessing the robustness of the
primary analysis results, and exploring the transitivity assumption.
Details
R-package rnmamod is built upon the WinBUGS program code found in
the series of tutorial papers on evidence synthesis methods for decision
making (Dias et al., 2013a; Dias et al., 2013b; Dias et al., 2013c) and
Dias et al. (2010) that introduces the node-splitting approach.
All models comprise Bayesian hierarchical models for one-stage network
meta-analysis and they are implemented in JAGS through the R-package
R2jags.
rnmamod comprises a suite of core models implemented in a
systematic review with multiple interventions:
fixed-effect and random-effects network meta-analysis
(run_model) based on Dias et al. (2013c);
fixed-effect and random-effects network meta-regression
(run_metareg) based on Cooper et al. (2009), and Dias et
al. (2013b);
fixed-effect and random-effects separate pairwise meta-analyses for
comparisons with at least two trials (run_series_meta);
local evaluation of the consistency assumption using the
fixed-effect or random-effects node-splitting approach
(run_nodesplit) based on Dias et al. (2010), and
van Valkenhoef et al. (2016);
global evaluation of the consistency assumption using the
fixed-effect or random-effects unrelated mean effects model
(run_ume) based on Dias et al. (2013a) and Spineli (2021);
comprehensive sensitivity analysis for the impact of aggregate
binary and continuous missing participant outcome data
(run_sensitivity) based on Spineli et al. (2021a);
hierarchical baseline model for the selected reference intervention
(baseline_model) based in Dias et al. (2013d).
rnmamod also includes a rich suite of visualisation tools to aid in
the interpretation of the results and preparation of the manuscript for
submission:
network plot and description of the evidence base
(netplot and describe_network,
respectively) following the PRISMA statement for systematic reviews with
network meta-analysis (Hutton et al., 2015);
illustration of the R-hat (Gelman and Rubin, 1992) and MCMC error
for all monitored nodes and creation of an HTML file with a panel of
diagnostic plots for each monitored parameter
(mcmc_diagnostics);
heatmap on the proportion of missing participants across the network
(heatmap_missing_network) and across the intervention arms
of each trial in the dataset (heatmap_missing_dataset);
league heatmap with the estimated and predicted summary effects of
all possible pairwise comparisons in the network and integrated SUCRA
(Salanti et al., 2011) or P-scores (Ruecker and Schwarzer, 2015)
(league_heatmap and
league_heatmap_pred, respectively) after performing network
meta-analysis or network meta-regression;
league table for relative and absolute effects for all pairwise
comparisons and interventions when conducting network meta-analysis anew
via the package (league_table_absolute) or using the results
of a published systematic review with network meta-analysis
(league_table_absolute_user);
forest plot with the trial-specific and summary absolute risks when
employing the hierarchical baseline model for the selected reference
intervention (baseline_model) as described in
Dias et al. (2013d);
rankograms with integrated SUCRA values for each intervention in
the network (rankosucra_plot) after performing network
meta-analysis (Salanti et al., 2011);
forest plot with the estimated and predicted summary effects of all
comparisons with a selected intervention (forestplot) as
obtained from the network meta-analysis model, and a forest plot with the
corresponding SUCRA values (Salanti et al., 2011);
tabulation of the estimated regression coefficient(s), the estimated
and predicted summary effects, measures of model fit and estimated
between-trial standard deviation before and after adjusting for a
trial-specific covariate (metareg_plot), and visualisation
of the summary effects and SUCRA values from both models
(forestplot_metareg, and scatterplot_sucra,
respectively–both found in metareg_plot);
tabulation of the estimated direct and indirect effects of the split
nodes and corresponding inconsistency factors, measures of model fit and
estimated between-trial standard deviation after each split node, and
visualisation of these results (nodesplit_plot);
tabulation of the estimated summary effects of all comparisons
observed in the network, measures of model fit and estimated between-trial
standard deviation under the unrelated mean effects model and network
meta-analysis, as well as visualisation of the summary effects from both
models (intervalplot_panel_ume) and the goodness of fit of
each model using a series of complementary plots
(scatterplots_dev (Dias et al., 2013a),
bland_altman_plot (Bland and Altman, 1999), and
leverage_plot (Dias et al., 2010)–all found in
ume_plot);
tabulation of the estimated summary effects and corresponding
between-trial standard deviation for comparisons with at least two trials
under pairwise and network meta-analysis, as well as visualisation of
these results (series_meta_plot);
calculation and visualisation of the robustness index for all
possible comparisons in the network (robustness_index,
robustness_index_user and heatmap_robustness)
(Spineli et al., 2021a);
enhanced balloon plot with the summary effects and between-trial
standard deviation for a selected pairwise comparison under several
scenarios about the missingness parameter (balloon_plot)
(Spineli et al., 2021a);
barplot with the Kullback-Leibler divergence measure from each
informative scenario to the missing-at-random assumption about the
missingness parameter for a selected pairwise comparison
(kld_barplot) (Spineli et al., 2021a).
rnmamod also assists the researcher in assessing the transitivity
assumption quantitatively based on trial dissimilarities for various
trial-level aggregate participant and methodological characteristics
calculated using the Gower's dissimilarity coefficient
(gower_distance and comp_clustering) (Gower,
1971). Results on the clustered comparisons based on hierarchical
agglomerative clustering are illustrated using a dendrogram with integrated
heatmap (dendro_heatmap). The distribution of the
characteristics is presented using violin plots with integrated box
plots and dots, and stacked bar plots across the observed treatment
comparisons (distr_characteristics). Missing data in the
characteristics across the trials and observed comparisons are visualised
using bar plots and tile plot (miss_characteristics).
Missing participant outcome data are addressed in all models of the package
after extending the code to incorporate the pattern-mixture model
(Spineli et al., 2021b; Spineli, 2019).
Type citation("rnmamod") on how to cite rnmamod.
To report possible bugs and errors, send an email to Loukia Spineli
(Spineli.Loukia@mh-hannove.de).
The development version of rnmamod is available on
GitHub under the
GPL-3.0 License.
Author(s)
Loukia M. Spineli
References
Bland JM, Altman DG. Measuring agreement in method comparison studies.
Stat Methods Med Res 1999;8(2):135–60.
doi: 10.1177/096228029900800204
Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ. Addressing between-study
heterogeneity and inconsistency in mixed treatment comparisons: Application
to stroke prevention treatments in individuals with non-rheumatic atrial
fibrillation. Stat Med 2009;28(14):1861–81.
doi: 10.1002/sim.3594
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 2013a;33(5):641–56.
doi: 10.1177/0272989X12455847
Dias S, Sutton AJ, Welton NJ, Ades AE. Evidence synthesis for decision making
3: heterogeneity–subgroups, meta-regression, bias, and bias-adjustment.
Med Decis Making 2013b;33(5):618–40.
doi: 10.1177/0272989X13485157
Dias S, Sutton AJ, Ades AE, Welton NJ. Evidence synthesis for decision
making 2: a generalized linear modeling framework for pairwise and network
meta-analysis of randomized controlled trials. Med Decis Making
2013c;33(5):607–17. doi: 10.1177/0272989X12458724
Dias S, Welton NJ, Sutton AJ, Ades AE. Evidence synthesis for decision
making 5: the baseline natural history model. Med Decis Making
2013d;33(5):657–70. doi: 10.1177/0272989X13485155
Dias S, Welton NJ, Caldwell DM, Ades AE. Checking consistency in mixed
treatment comparison meta-analysis.
Stat Med 2010;29(7-8):932–44.
doi: 10.1002/sim.3767
Gelman, A, Rubin, DB. Inference from iterative simulation using multiple
sequences. Stat Sci 1992;7(4):457–72.
doi: 10.1214/ss/1177011136
Gower JC. A General Coefficient of Similarity and Some of Its Properties.
Biometrics 1971;27(4):857–71.
http://dx.doi.org/10.2307/2528823
Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al.
The PRISMA extension statement for reporting of systematic reviews
incorporating network meta-analyses of health care interventions: checklist
and explanations. Ann Intern Med 2015;162(11):777–84.
doi: 10.7326/M14-2385
Ruecker G, Schwarzer G. Ranking treatments in frequentist network
meta-analysis works without resampling methods.
BMC Med Res Methodol 2015;15:58.
doi: 10.1186/s12874-015-0060-8
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
Spineli LM. A revised framework to evaluate the consistency assumption
globally in a network of interventions. Med Decis Making 2021.
doi: 10.1177/0272989X211068005
Spineli LM, Kalyvas C, Papadimitropoulou K. Quantifying the robustness of
primary analysis results: A case study on missing outcome data in pairwise
and network meta-analysis.
Res Synth Methods 2021a;12(4):475–90. doi: 10.1002/jrsm.1478
Spineli LM, Kalyvas C, Papadimitropoulou K. Continuous(ly) missing outcome
data in network meta-analysis: a one-stage pattern-mixture model approach.
Stat Methods Med Res 2021b;30(4):958–75.
doi: 10.1177/0962280220983544
Spineli LM. An empirical comparison of Bayesian modelling strategies for
missing binary outcome data in network meta-analysis.
BMC Med Res Methodol 2019;19(1):86.
doi: 10.1186/s12874-019-0731-y
van Valkenhoef G, Dias S, Ades AE, Welton NJ. Automated generation of
node-splitting models for assessment of inconsistency in network
meta-analysis. Res Synth Methods 2016;7(1):80–93.
doi: 10.1002/jrsm.1167
rnmamod documentation built on May 29, 2024, 2:44 a.m.
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| rnmamod-package | R Documentation |
rnmamod: Bayesian Network Meta-analysis with Missing Participants
Description
An R package for performing Bayesian network meta-analysis while handling missing participant outcome data properly, assessing the robustness of the primary analysis results, and exploring the transitivity assumption.
Details
R-package rnmamod is built upon the WinBUGS program code found in the series of tutorial papers on evidence synthesis methods for decision making (Dias et al., 2013a; Dias et al., 2013b; Dias et al., 2013c) and Dias et al. (2010) that introduces the node-splitting approach. All models comprise Bayesian hierarchical models for one-stage network meta-analysis and they are implemented in JAGS through the R-package R2jags.
rnmamod comprises a suite of core models implemented in a systematic review with multiple interventions:
fixed-effect and random-effects network meta-analysis (
run_model) based on Dias et al. (2013c);fixed-effect and random-effects network meta-regression (
run_metareg) based on Cooper et al. (2009), and Dias et al. (2013b);fixed-effect and random-effects separate pairwise meta-analyses for comparisons with at least two trials (
run_series_meta);local evaluation of the consistency assumption using the fixed-effect or random-effects node-splitting approach (
run_nodesplit) based on Dias et al. (2010), and van Valkenhoef et al. (2016);global evaluation of the consistency assumption using the fixed-effect or random-effects unrelated mean effects model (
run_ume) based on Dias et al. (2013a) and Spineli (2021);comprehensive sensitivity analysis for the impact of aggregate binary and continuous missing participant outcome data (
run_sensitivity) based on Spineli et al. (2021a);hierarchical baseline model for the selected reference intervention (
baseline_model) based in Dias et al. (2013d).
rnmamod also includes a rich suite of visualisation tools to aid in the interpretation of the results and preparation of the manuscript for submission:
network plot and description of the evidence base (
netplotanddescribe_network, respectively) following the PRISMA statement for systematic reviews with network meta-analysis (Hutton et al., 2015);illustration of the R-hat (Gelman and Rubin, 1992) and MCMC error for all monitored nodes and creation of an HTML file with a panel of diagnostic plots for each monitored parameter (
mcmc_diagnostics);heatmap on the proportion of missing participants across the network (
heatmap_missing_network) and across the intervention arms of each trial in the dataset (heatmap_missing_dataset);league heatmap with the estimated and predicted summary effects of all possible pairwise comparisons in the network and integrated SUCRA (Salanti et al., 2011) or P-scores (Ruecker and Schwarzer, 2015) (
league_heatmapandleague_heatmap_pred, respectively) after performing network meta-analysis or network meta-regression;league table for relative and absolute effects for all pairwise comparisons and interventions when conducting network meta-analysis anew via the package (
league_table_absolute) or using the results of a published systematic review with network meta-analysis (league_table_absolute_user);forest plot with the trial-specific and summary absolute risks when employing the hierarchical baseline model for the selected reference intervention (
baseline_model) as described in Dias et al. (2013d);rankograms with integrated SUCRA values for each intervention in the network (
rankosucra_plot) after performing network meta-analysis (Salanti et al., 2011);forest plot with the estimated and predicted summary effects of all comparisons with a selected intervention (
forestplot) as obtained from the network meta-analysis model, and a forest plot with the corresponding SUCRA values (Salanti et al., 2011);tabulation of the estimated regression coefficient(s), the estimated and predicted summary effects, measures of model fit and estimated between-trial standard deviation before and after adjusting for a trial-specific covariate (
metareg_plot), and visualisation of the summary effects and SUCRA values from both models (forestplot_metareg, andscatterplot_sucra, respectively–both found inmetareg_plot);tabulation of the estimated direct and indirect effects of the split nodes and corresponding inconsistency factors, measures of model fit and estimated between-trial standard deviation after each split node, and visualisation of these results (
nodesplit_plot);tabulation of the estimated summary effects of all comparisons observed in the network, measures of model fit and estimated between-trial standard deviation under the unrelated mean effects model and network meta-analysis, as well as visualisation of the summary effects from both models (
intervalplot_panel_ume) and the goodness of fit of each model using a series of complementary plots (scatterplots_dev(Dias et al., 2013a),bland_altman_plot(Bland and Altman, 1999), andleverage_plot(Dias et al., 2010)–all found inume_plot);tabulation of the estimated summary effects and corresponding between-trial standard deviation for comparisons with at least two trials under pairwise and network meta-analysis, as well as visualisation of these results (
series_meta_plot);calculation and visualisation of the robustness index for all possible comparisons in the network (
robustness_index,robustness_index_userandheatmap_robustness) (Spineli et al., 2021a);enhanced balloon plot with the summary effects and between-trial standard deviation for a selected pairwise comparison under several scenarios about the missingness parameter (
balloon_plot) (Spineli et al., 2021a);barplot with the Kullback-Leibler divergence measure from each informative scenario to the missing-at-random assumption about the missingness parameter for a selected pairwise comparison (
kld_barplot) (Spineli et al., 2021a).
rnmamod also assists the researcher in assessing the transitivity
assumption quantitatively based on trial dissimilarities for various
trial-level aggregate participant and methodological characteristics
calculated using the Gower's dissimilarity coefficient
(gower_distance and comp_clustering) (Gower,
1971). Results on the clustered comparisons based on hierarchical
agglomerative clustering are illustrated using a dendrogram with integrated
heatmap (dendro_heatmap). The distribution of the
characteristics is presented using violin plots with integrated box
plots and dots, and stacked bar plots across the observed treatment
comparisons (distr_characteristics). Missing data in the
characteristics across the trials and observed comparisons are visualised
using bar plots and tile plot (miss_characteristics).
Missing participant outcome data are addressed in all models of the package after extending the code to incorporate the pattern-mixture model (Spineli et al., 2021b; Spineli, 2019).
Type citation("rnmamod") on how to cite rnmamod.
To report possible bugs and errors, send an email to Loukia Spineli (Spineli.Loukia@mh-hannove.de).
The development version of rnmamod is available on GitHub under the GPL-3.0 License.
Author(s)
Loukia M. Spineli
References
Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8(2):135–60. doi: 10.1177/096228029900800204
Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ. Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: Application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med 2009;28(14):1861–81. doi: 10.1002/sim.3594
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 2013a;33(5):641–56. doi: 10.1177/0272989X12455847
Dias S, Sutton AJ, Welton NJ, Ades AE. Evidence synthesis for decision making 3: heterogeneity–subgroups, meta-regression, bias, and bias-adjustment. Med Decis Making 2013b;33(5):618–40. doi: 10.1177/0272989X13485157
Dias S, Sutton AJ, Ades AE, Welton NJ. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making 2013c;33(5):607–17. doi: 10.1177/0272989X12458724
Dias S, Welton NJ, Sutton AJ, Ades AE. Evidence synthesis for decision making 5: the baseline natural history model. Med Decis Making 2013d;33(5):657–70. doi: 10.1177/0272989X13485155
Dias S, Welton NJ, Caldwell DM, Ades AE. Checking consistency in mixed treatment comparison meta-analysis. Stat Med 2010;29(7-8):932–44. doi: 10.1002/sim.3767
Gelman, A, Rubin, DB. Inference from iterative simulation using multiple sequences. Stat Sci 1992;7(4):457–72. doi: 10.1214/ss/1177011136
Gower JC. A General Coefficient of Similarity and Some of Its Properties. Biometrics 1971;27(4):857–71. http://dx.doi.org/10.2307/2528823
Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med 2015;162(11):777–84. doi: 10.7326/M14-2385
Ruecker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol 2015;15:58. doi: 10.1186/s12874-015-0060-8
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
Spineli LM. A revised framework to evaluate the consistency assumption globally in a network of interventions. Med Decis Making 2021. doi: 10.1177/0272989X211068005
Spineli LM, Kalyvas C, Papadimitropoulou K. Quantifying the robustness of primary analysis results: A case study on missing outcome data in pairwise and network meta-analysis. Res Synth Methods 2021a;12(4):475–90. doi: 10.1002/jrsm.1478
Spineli LM, Kalyvas C, Papadimitropoulou K. Continuous(ly) missing outcome data in network meta-analysis: a one-stage pattern-mixture model approach. Stat Methods Med Res 2021b;30(4):958–75. doi: 10.1177/0962280220983544
Spineli LM. An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis. BMC Med Res Methodol 2019;19(1):86. doi: 10.1186/s12874-019-0731-y
van Valkenhoef G, Dias S, Ades AE, Welton NJ. Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis. Res Synth Methods 2016;7(1):80–93. doi: 10.1002/jrsm.1167
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