Multilevel Unanchored Meta-Regression for Indirect Treatment Comparisons

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Documentation for package ‘mlumr’ version 0.2.0

• 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
caries_agd	Dental caries: simulated comparator aggregate data (count)
caries_ipd	Dental caries: simulated index IPD (count)
check_diagnostics	Check the sampler diagnostics of a fit
check_identification	Can the aggregate data identify the comparator coefficients?
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_aux	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()'
default_prior_smooth	Default priors used by 'mlumr()'
dgamma	The Gamma distribution, parameterized by mean and standard deviation
distr	Specify a marginal distribution
dlogitnorm	The logit-Normal distribution
GammaDist	The Gamma distribution, parameterized by mean and standard deviation
geom_km	Observed Kaplan-Meier layer for survival overlays
logitNormal	The logit-Normal distribution
make_knots	Choose M-spline knots for a flexible-baseline survival model
marginal_effects	Marginal treatment effects
mlumr	Fit ML-UMR Model
mlumr_engine	Get or set the Stan engine
mlumr_forest	Forest plot of a small set of estimates
naive	Naive unadjusted indirect comparison
ndmm_agd	Newly diagnosed multiple myeloma: comparator pseudo-IPD (survival)
ndmm_agd_covs	Newly diagnosed multiple myeloma: comparator covariate summaries (survival)
ndmm_ipd	Newly diagnosed multiple myeloma: index individual patient data (survival)
pbern	Bernoulli CDF
pgamma	The Gamma distribution, parameterized by mean and standard deviation
plogitnorm	The logit-Normal distribution
plot.mlumr_conditional_effects	Plot covariate-conditional treatment effects
plot.mlumr_marginal_effects	Forest plot of population-standardized marginal effects
plot.mlumr_prediction	Plot absolute predictions from a fitted ML-UMR model
plot_prior_posterior	Prior-versus-posterior overlay
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
psoriasis_agd	Plaque psoriasis: comparator aggregate data (binary)
psoriasis_ipd	Plaque psoriasis: index individual patient data (binary)
qbern	Bernoulli quantile function
qgamma	The Gamma distribution, parameterized by mean and standard deviation
qlogitnorm	The logit-Normal distribution
set_agd	Set up aggregate data (AgD)
set_agd_surv	Set up aggregate survival data (reconstructed pseudo-IPD)
set_ipd	Set up individual patient data (IPD)
shoulder_agd	Shoulder pain: simulated comparator aggregate data (continuous)
shoulder_ipd	Shoulder pain: simulated index IPD (continuous)
stc	Simulated treatment comparison via G-computation
unnest_integration	Expand integration points into a long-format data frame