Package index
-
multinma-package
multinma
- multinma: A Package for Network Meta-Analysis of Individual and Aggregate Data in Stan
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set_agd_arm()
- Set up arm-based aggregate data
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set_agd_contrast()
- Set up contrast-based aggregate data
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set_agd_surv()
- Set up aggregate survival data
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set_ipd()
- Set up individual patient data
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combine_network()
- Combine multiple data sources into one network
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multi()
- Multinomial outcome data
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print(<nma_data>)
print(<mlnmr_data>)
- Print
nma_data
objects
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plot(<nma_data>)
- Network plots
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as.igraph(<nma_data>)
as_tbl_graph(<nma_data>)
- Convert networks to graph objects
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nma_data-class
nma_data
mlnmr_data
mlnmr_data-class
- The nma_data class
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is_network_connected()
- Check network connectedness
Setting up numerical integration (ML-NMR only)
Multilevel network meta-regression models require numerical integration points to be specified for the distributions of covariates in each aggregate data study in the network.
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add_integration()
unnest_integration()
- Add numerical integration points to aggregate data
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distr()
- Specify a general marginal distribution
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qlogitnorm()
dlogitnorm()
plogitnorm()
- The logit Normal distribution
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normal()
half_normal()
log_normal()
cauchy()
half_cauchy()
student_t()
half_student_t()
log_student_t()
exponential()
flat()
- Prior distributions
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summary(<nma_prior>)
- Summary of prior distributions
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nma_prior-class
nma_prior
- The nma_prior class
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plot_prior_posterior()
- Plot prior vs posterior distribution
Model fitting
Model specification and fitting is accomplished using the nma()
function.
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nma()
- Network meta-analysis models
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print(<stan_nma>)
- Print
stan_nma
objects
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summary(<stan_nma>)
plot(<stan_nma>)
- Posterior summaries from
stan_nma
objects
-
pairs(<stan_nma>)
- Matrix of plots for a
stan_nma
object
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stan_nma-class
stan_nma
stan_mlnmr
- The stan_nma class
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adapt_delta
- Target average acceptance probability
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RE_cor()
which_RE()
- Random effects structure
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.default()
.is_default()
- Set default values
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plot_prior_posterior()
- Plot prior vs posterior distribution
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plot_integration_error()
- Plot numerical integration error
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dic()
- Deviance Information Criterion (DIC)
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print(<nma_dic>)
- Print DIC details
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plot(<nma_dic>)
- Plots of model fit diagnostics
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nma_dic-class
nma_dic
- The nma_dic class
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loo(<stan_nma>)
waic(<stan_nma>)
- Model comparison using the
loo
package
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get_nodesplits()
has_direct()
has_indirect()
- Direct and indirect evidence
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nma_nodesplit-class
nma_nodesplit
nma_nodesplit_df
nma_nodesplit_df-class
- The nma_nodesplit class
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print(<nma_nodesplit_df>)
print(<nma_nodesplit>)
- Print
nma_nodesplit_df
objects
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summary(<nma_nodesplit_df>)
summary(<nma_nodesplit>)
plot(<nma_nodesplit>)
plot(<nma_nodesplit_df>)
- Summarise the results of node-splitting models
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nodesplit_summary-class
nodesplit_summary
- The
nodesplit_summary
class
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print(<nodesplit_summary>)
as_tibble(<nodesplit_summary>)
as.tibble.nodesplit_summary()
as.data.frame(<nodesplit_summary>)
- Methods for
nodesplit_summary
objects
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plot(<nodesplit_summary>)
- Plots of node-splitting models
Posterior summaries and working with fitted models
Producing and plotting relative effects, absolute predictions, marginal effects, posterior ranks and rank probabilities. Converting to MCMC arrays and matrices.
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relative_effects()
- Relative treatment effects
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marginal_effects()
- Marginal treatment effects
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predict(<stan_nma>)
predict(<stan_nma_surv>)
- Predictions of absolute effects from NMA models
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posterior_ranks()
posterior_rank_probs()
- Treatment rankings and rank probabilities
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print(<nma_summary>)
as.data.frame(<nma_summary>)
as.tibble(<nma_summary>)
as_tibble(<nma_summary>)
as.array(<nma_summary>)
as.matrix(<nma_summary>)
as.array(<nma_rank_probs>)
as.matrix(<nma_rank_probs>)
- Methods for
nma_summary
objects
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plot(<nma_summary>)
plot(<nma_parameter_summary>)
plot(<nma_rank_probs>)
plot(<surv_nma_summary>)
- Plots of summary results
-
nma_summary-class
nma_summary
nma_rank_probs
- The
nma_summary
class
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as.array(<stan_nma>)
as.data.frame(<stan_nma>)
as_tibble(<stan_nma>)
as.tibble(<stan_nma>)
as.matrix(<stan_nma>)
- Convert samples into arrays, matrices, or data frames
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summary(<mcmc_array>)
print(<mcmc_array>)
plot(<mcmc_array>)
names(<mcmc_array>)
`names<-`(<mcmc_array>)
- Working with 3D MCMC arrays
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as.stanfit()
- as.stanfit
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dmspline()
pmspline()
qmspline()
hmspline()
Hmspline()
rmst_mspline()
- Distribution functions for M-spline baseline hazards
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make_knots()
- Knot locations for M-spline baseline hazard models
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theme_multinma()
- Plot theme for multinma plots
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geom_km()
- Kaplan-Meier curves of survival data
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atrial_fibrillation
- Stroke prevention in atrial fibrillation patients
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bcg_vaccine
- BCG vaccination
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blocker
- Beta blockers to prevent mortality after MI
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diabetes
- Incidence of diabetes in trials of antihypertensive drugs
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dietary_fat
- Reduced dietary fat to prevent mortality
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hta_psoriasis
- HTA Plaque Psoriasis
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ndmm_ipd
ndmm_agd
ndmm_agd_covs
- Newly diagnosed multiple myeloma
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parkinsons
- Mean off-time reduction in Parkison's disease
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plaque_psoriasis_ipd
plaque_psoriasis_agd
- Plaque psoriasis data
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smoking
- Smoking cessation data
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statins
- Statins for cholesterol lowering
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thrombolytics
- Thrombolytic treatments data
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transfusion
- Granulocyte transfusion in patients with neutropenia or neutrophil dysfunction
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example_ndmm
- Example newly-diagnosed multiple myeloma
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example_pso_mlnmr
- Example plaque psoriasis ML-NMR
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example_smk_fe
- Example smoking FE NMA
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example_smk_nodesplit
- Example smoking node-splitting
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example_smk_re
- Example smoking RE NMA
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example_smk_ume
- Example smoking UME NMA