Thanks to Kit Baum, an updated midas package is now available through SSC archive. MIDAS (Meta-analytical Integration of Diagnostic Accuracy Studies) provides a comprehensive suite of commands for bivariate meta-analysis of diagnostic test accuracy data.
The package includes:
• 5 estimation methods: maximum likelihood (meglm), quasi-random simulated likelihood, Bayesian Metropolis-Hastings (bayesmh), Hamiltonian Monte Carlo (CmdStan), and integrated nested Laplace approximation (R-INLA)
• 8 exploratory commands: QUADAS/QUADAS-2 quality assessment, bivariate boxplots, chi-plots, Kendall concordance, assessment diagnostics, binomial sample size estimation, and exploratory forest plots
• 9 post-estimation commands: summary forest plots (4 plot types), SROC curves (regressional and bivariate), Fagan nomograms, likelihood ratio matrices, conditional probability plots, publication bias testing, Bayesian diagnostic plots, and clinical utility analysis (HSRUC)
• 2 heterogeneity investigation commands: stratified subgroup analysis and bivariate meta-regression with comparative SROC plots
• 5 data conversion utilities: simulation, ordinal-to-binary, continuous-to-binary, cluster-to-binary, and IPD-to-aggregate
• GUI dialogs for 29 subcommands
The package requires Stata 16+ and community-contributed bayesparallel, xsvmat and moremata packages. External software (R-INLA, CmdStan) is needed only for the inla and hmc estimators.
Ben A. Dwamena
The package includes:
• 5 estimation methods: maximum likelihood (meglm), quasi-random simulated likelihood, Bayesian Metropolis-Hastings (bayesmh), Hamiltonian Monte Carlo (CmdStan), and integrated nested Laplace approximation (R-INLA)
• 8 exploratory commands: QUADAS/QUADAS-2 quality assessment, bivariate boxplots, chi-plots, Kendall concordance, assessment diagnostics, binomial sample size estimation, and exploratory forest plots
• 9 post-estimation commands: summary forest plots (4 plot types), SROC curves (regressional and bivariate), Fagan nomograms, likelihood ratio matrices, conditional probability plots, publication bias testing, Bayesian diagnostic plots, and clinical utility analysis (HSRUC)
• 2 heterogeneity investigation commands: stratified subgroup analysis and bivariate meta-regression with comparative SROC plots
• 5 data conversion utilities: simulation, ordinal-to-binary, continuous-to-binary, cluster-to-binary, and IPD-to-aggregate
• GUI dialogs for 29 subcommands
The package requires Stata 16+ and community-contributed bayesparallel, xsvmat and moremata packages. External software (R-INLA, CmdStan) is needed only for the inla and hmc estimators.
Ben A. Dwamena

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