Multilevel Unanchored Meta-Regression for Indirect Treatment Comparisons

---

Documentation for package ‘mlumr’ version 0.1.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

add_integration	Add numerical integration points
calculate_dic	Calculate DIC for model comparison
calculate_loo	Calculate LOO-CV for an mlumr_fit
calculate_waic	Calculate WAIC for an mlumr_fit
check_integration	Check integration point adequacy
combine_data	Combine IPD and AgD for unanchored comparison
compare_models	Compare fitted ML-UMR models
conditional_effects	Conditional treatment effects
conditional_predict	Conditional predictions
dbern	Bernoulli PMF
default_priors	Default priors used by 'mlumr()'
default_prior_beta	Default priors used by 'mlumr()'
default_prior_intercept	Default priors used by 'mlumr()'
default_prior_sigma	Default priors used by 'mlumr()'
distr	Specify a marginal distribution
marginal_effects	Marginal treatment effects
mlumr	Fit ML-UMR Model
mlumr_engine	Get or Set the Stan Engine
naive	Naive unadjusted indirect comparison
pbern	Bernoulli CDF
predict.mlumr_fit	Predictions from ML-UMR model
prior_cauchy	Specify a Cauchy prior
prior_exponential	Specify an exponential prior
prior_normal	Specify a normal prior
prior_sensitivity	Prior sensitivity analysis for an ML-UMR fit
prior_student_t	Specify a Student-t prior
prior_summary	Summary of priors used by a fitted ML-UMR model
prior_summary.default	Summary of priors used by a fitted ML-UMR model
prior_summary.mlumr_fit	Summary of priors used by a fitted ML-UMR model
qbern	Bernoulli quantile function
set_agd	Set up aggregate data (AgD)
set_ipd	Set up individual patient data (IPD)
stc	Simulated treatment comparison via G-computation
unnest_integration	Expand integration points into a long-format data frame