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Overview of Examples

Source: vignettes/vignette_overview.Rmd
vignette_overview.Rmd

This package contains a number of vignettes, each one walking through an example analysis. The table below gives an overview.

Many of these examples recreate analyses from the series of Technical Support Documents published by the NICE Decision Support Unit (Dias et al. 2011). The exceptions are atrial fibrillation (Cooper et al. 2009), white blood cell transfusion (Turner et al. 2012), social anxiety (Perren et al. 2025; Mayo-Wilson et al. 2014), and plaque psoriasis multilevel network meta-regression (Phillippo et al. 2020, 2022).

Title Outcome type Likelihood Link function Notable features
Blocker Counts Binomial logit Pairwise MA
Dietary fat Rates Poisson log Analysis of log rate ratios from rate data
Diabetes Counts with time at risk Binomial cloglog Analysis of log hazard ratios from count data with time at risk
Parkinson’s Continuous Normal Identity Analysis of arm-based data, contrast-based data, and a mixture of both
HTA plaque psoriasis Ordered Multinomial (ordered) probit Analysis of ordered categorical outcomes
Statins Counts Binomial logit Meta-regression with subgroups
BCG vaccine Counts Binomial logit Meta-regression with a continuous covariate, predictive distributions
Smoking cessation Counts Binomial logit Assessing inconsistency with unrelated mean effects and node-splitting models
Thrombolytics Counts Binomial logit Assessing inconsistency with unrelated mean effects and node-splitting models
Atrial fibrillation Counts Binomial logit Meta-regression with shared class interactions
WBC transfusion Counts Binomial logit Informative log-Normal prior on \tau^2
ML-NMR plaque psoriasis Binary (IPD) and counts (AgD), and ordered categorical Bernoulli (IPD) and two-parameter Binomial (AgD), and ordered multinomial probit Multilevel network meta-regression combining IPD and AgD
ML-NMR newly diagnosed multiple myeloma Time-to-event with censoring M-spline baseline hazard log Multilevel network meta-regression combining IPD and AgD
Social anxiety Continuous Normal Identity Model selection with class effects models
Certolizumab Counts Binomial logit Baseline risk meta-regression

References

Cooper, N. J., A. J. Sutton, D. Morris, A. E. Ades, and N. J. Welton. 2009. “Addressing Between-Study Heterogeneity and Inconsistency in Mixed Treatment Comparisons: Application to Stroke Prevention Treatments in Individuals with Non-Rheumatic Atrial Fibrillation.” Statistics in Medicine 28 (14): 1861–81. https://doi.org/10.1002/sim.3594.
Dias, S., N. J. Welton, A. J. Sutton, et al. 2011. NICE DSU Technical Support Documents 1-7: Evidence Synthesis for Decision Making. National Institute for Health and Care Excellence. https://sheffield.ac.uk/nice-dsu.
Mayo-Wilson, Evan, Sofia Dias, Ifigeneia Mavranezouli, et al. 2014. “Psychological and Pharmacological Interventions for Social Anxiety Disorder in Adults: A Systematic Review and Network Meta-Analysis.” The Lancet Psychiatry 1 (5): 368–76.
Perren, Samuel J., Hugo Pedder, Nicky J. Welton, and David M. Phillippo. 2025. “Network Meta-Analysis with Class Effects: A Practical Guide and Model Selection Algorithm.” Medical Decision Making 46 (3): 275–95. https://doi.org/10.1177/0272989x251389887.
Phillippo, D. M., S. Dias, A. E. Ades, et al. 2020. “Multilevel Network Meta-Regression for Population-Adjusted Treatment Comparisons.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 183 (3): 1189–210. https://doi.org/10.1111/rssa.12579.
Phillippo, D. M., S. Dias, A. E. Ades, et al. 2022. “Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-Regression to a Network of Treatments for Plaque Psoriasis.” Medical Decision Making, ahead of print. https://doi.org/10.1177/0272989X221117162.
Turner, R. M., J. Davey, M. J. Clarke, S. G. Thompson, and J. P. T. Higgins. 2012. “Predicting the Extent of Heterogeneity in Meta-Analysis, Using Empirical Data from the Cochrane Database of Systematic Reviews.” International Journal of Epidemiology 41 (3): 818–27. https://doi.org/10.1093/ije/dys041.