Calling example("example_ndmm") will run a proportional
hazards Weibull NMA model on the newly-diagnosed multiple myeloma data,
using the code in the Examples section below. The resulting stan_nma
object ndmm_fit will then be available in the global environment.
Examples
# Set up newly-diagnosed multiple myeloma network
head(ndmm_ipd)
#> study trt studyf trtf age iss_stage3 response_cr_vgpr male
#> 1 McCarthy2012 Pbo McCarthy2012 Pbo 50.81625 0 1 0
#> 2 McCarthy2012 Pbo McCarthy2012 Pbo 62.18165 0 0 0
#> 3 McCarthy2012 Pbo McCarthy2012 Pbo 51.53762 1 1 1
#> 4 McCarthy2012 Pbo McCarthy2012 Pbo 46.74128 0 1 1
#> 5 McCarthy2012 Pbo McCarthy2012 Pbo 62.62561 0 1 1
#> 6 McCarthy2012 Pbo McCarthy2012 Pbo 49.24520 1 1 0
#> eventtime status
#> 1 31.106516 1
#> 2 3.299623 0
#> 3 57.400000 0
#> 4 57.400000 0
#> 5 57.400000 0
#> 6 30.714460 0
head(ndmm_agd)
#> study studyf trt trtf eventtime status
#> 1 Morgan2012 Morgan2012 Pbo Pbo 18.72575 1
#> 2 Morgan2012 Morgan2012 Pbo Pbo 63.36000 0
#> 3 Morgan2012 Morgan2012 Pbo Pbo 34.35726 1
#> 4 Morgan2012 Morgan2012 Pbo Pbo 10.77826 1
#> 5 Morgan2012 Morgan2012 Pbo Pbo 63.36000 0
#> 6 Morgan2012 Morgan2012 Pbo Pbo 14.52966 1
ndmm_net <- combine_network(
set_ipd(ndmm_ipd,
study, trt,
Surv = Surv(eventtime / 12, status)),
set_agd_surv(ndmm_agd,
study, trt,
Surv = Surv(eventtime / 12, status),
covariates = ndmm_agd_covs))
# \donttest{
# Fit Weibull (PH) model
ndmm_fit <- nma(ndmm_net, refresh = if (interactive()) 200 else 0,
likelihood = "weibull",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 10),
prior_aux = half_normal(scale = 10))
#> Note: Setting "Pbo" as the network reference treatment.
ndmm_fit
#> A fixed effects NMA with a weibull likelihood (log link).
#> Inference for Stan model: survival_param.
#> 4 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#>
#> mean se_mean sd 2.5% 25% 50% 75%
#> d[Len] -0.54 0.00 0.05 -0.62 -0.57 -0.54 -0.51
#> d[Thal] -0.11 0.00 0.09 -0.28 -0.17 -0.11 -0.06
#> lp__ -6229.88 0.06 2.40 -6235.28 -6231.31 -6229.58 -6228.13
#> shape[Attal2012] 1.30 0.00 0.06 1.18 1.26 1.30 1.34
#> shape[Jackson2019] 0.93 0.00 0.02 0.89 0.92 0.93 0.95
#> shape[McCarthy2012] 1.29 0.00 0.07 1.17 1.25 1.29 1.34
#> shape[Morgan2012] 0.88 0.00 0.03 0.82 0.86 0.88 0.90
#> shape[Palumbo2014] 1.02 0.00 0.07 0.88 0.97 1.01 1.06
#> 97.5% n_eff Rhat
#> d[Len] -0.45 5032 1
#> d[Thal] 0.06 5954 1
#> lp__ -6226.07 1602 1
#> shape[Attal2012] 1.42 5073 1
#> shape[Jackson2019] 0.98 5326 1
#> shape[McCarthy2012] 1.43 4660 1
#> shape[Morgan2012] 0.94 5085 1
#> shape[Palumbo2014] 1.16 4929 1
#>
#> Samples were drawn using NUTS(diag_e) at Wed Mar 6 13:02:19 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# }
# \dontshow{
if (requireNamespace("pkgdown", quietly = TRUE) && pkgdown::in_pkgdown()) {
assign("ndmm_net", ndmm_net, .GlobalEnv)
assign("ndmm_fit", ndmm_fit, .GlobalEnv)
}
# }