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Run stan to generate a PoS prediction

Source: R/run_stan.R
run_stan.Rd

A wrapper function to run stan in order to generate a PoS prediction

Usage

run_stan(
  target_hr,
  J,
  nevents3,
  hr_bound,
  omega,
  est_obs_pfs,
  low_obs_pfs,
  upp_obs_pfs,
  obs_pfs_conf_level = 0.95,
  thres = 0.01,
  n_trt2,
  n_ctrl2,
  n_resp_trt2,
  n_resp_ctrl2,
  use_orr = FALSE,
  use_pfs = FALSE,
  het_degree_p2 = "small",
  het_degree_p3 = "very small",
  ratio = 1,
  m_0 = NA,
  m_1 = NA,
  nu_0 = NA,
  nu_1 = NA,
  lm_sd = NA,
  niter = 1000,
  nchains = 4,
  ncores = 4,
  seed,
  ...
)

Arguments

target_hr

target hazard ration in phase 3 study (i.e., under alternative hypothesis)

J

number of planned analyses in phase 3 study

nevents3

numeric vector of target number of events in phase 3 study

hr_bound

numeric vector of hazard ratio bounds for analyses in phase 3 study

omega

probability that a treatment effect comes from a enthusiastic prior component, i.e., initial benchmarking probability for the study success

est_obs_pfs

estimated PFS hazard ratio based on prior/earlier study

low_obs_pfs

lower bound of estimated PFS hazard ratio based on prior/earlier study

upp_obs_pfs

upper bound of estimated PFS hazard ratio based on prior/earlier study

obs_pfs_conf_level

confidence level for the estimated PFS hazard ratio bounds, Default: 0.95

thres

probability of either lack treatment effect under the enthusiastic prior or substantial treatment effect under the pessimistic prior. Should be set to a small value, close to 0, Default: 0.01

n_trt2

sample size in treatment arm from a prior/earlier study

n_ctrl2

sample size in control arm from a prior/earlier study

n_resp_trt2

number of responses in treatment arm from a prior/earlier study

n_resp_ctrl2

number of responses in control arm from a prior/earlier study

use_orr

whether response data from a prior/earlier study should be used, Default: FALSE

use_pfs

whether PFS data from a prior/earlier study should be used, Default: FALSE

het_degree_p2

indicates the degree of heterogeneity of a study level parameter for a prior/earlier study and must be one of "large", "substantial", "moderate", "small", "very small", Default: 'small'.

het_degree_p3

indicates the degree of heterogeneity of a study level parameter for a phase 3 study and must be one of "large", "substantial", "moderate", "small", "very small", Default: 'very small'

ratio

randomization ratio of experimental arm compared to control

m_0

intercept for linear regression of log treatment effect of PFS on log treatment effect on response. A value is expected only when use_orr = TRUE and use_pfs = TRUE, Default: NA

m_1

slope for linear regression of log treatment effect of PFS on log treatment effect on response. A value is expected only when use_orr = TRUE and use_pfs = TRUE, Default: NA

nu_0

standard error of m_0. A value is expected only when use_orr = TRUE and use_pfs = TRUE, Default: NA

nu_1

standard error of m_1. A value is expected only when use_orr = TRUE and use_pfs = TRUE, Default: NA

lm_sd

linear regression residual variance of log treatment effect of PFS on log treatment effect on response. A value is expected only when use_orr = TRUE and use_pfs = TRUE, Default: NA

niter

number of iterations to be used in stan run, Default: 1000

nchains

number of chains to be used in stan run, Default: 4

ncores

number of cores to be used in stan run, Default: 4

seed

seed to be used in stan run

...

params to pass to stan run

Value

list which includes the generated stan object, list of data that was supplied to rstan::stan(), and the name of the stan file which was run

Specification

The contents of this section are shown in PDF user manual only.

See also

Normal erf stan

Examples

# use PFS data from a prior study
  run_stan(
    target_hr = 0.7, 
    J = 2, 
    nevents3 = c(370, 468), 
    hr_bound = c(0.779, 0.8204),
    omega = 0.5,
    est_obs_pfs = 0.88,
    low_obs_pfs = 0.74,
    upp_obs_pfs = 1.05,
    use_pfs = TRUE,
    seed = 325,
    ncores = 1,
    nchains = 1)
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
#> Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
#> Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
#> Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
#> Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.017 seconds (Warm-up)
#> Chain 1:                0.009 seconds (Sampling)
#> Chain 1:                0.026 seconds (Total)
#> Chain 1: 
#> $fit_rstan
#> Inference for Stan model: anon_model.
#> 1 chains, each with iter=1000; warmup=500; thin=1; 
#> post-warmup draws per chain=500, total post-warmup draws=500.
#> 
#>                  mean se_mean   sd   2.5%   25%   50%   75% 97.5% n_eff Rhat
#> mu_P            -0.14    0.01 0.11  -0.35 -0.22 -0.14 -0.06  0.08   200    1
#> tau_P2           0.07    0.00 0.06   0.00  0.03  0.06  0.11  0.19   333    1
#> tau_P3           0.04    0.00 0.03   0.00  0.02  0.03  0.06  0.11   433    1
#> theta_P2_raw     0.13    0.06 1.00  -1.77 -0.52  0.12  0.80  2.02   277    1
#> theta_P3_raw     0.01    0.05 0.99  -1.87 -0.67  0.04  0.71  2.03   351    1
#> theta_P2        -0.13    0.00 0.09  -0.29 -0.19 -0.13 -0.07  0.03   395    1
#> theta_P3        -0.14    0.01 0.12  -0.36 -0.23 -0.13 -0.06  0.09   237    1
#> theta_P3_hat[1] -0.14    0.01 0.14  -0.39 -0.23 -0.13 -0.05  0.15   289    1
#> theta_P3_hat[2] -0.14    0.01 0.13  -0.38 -0.22 -0.14 -0.05  0.13   315    1
#> vec_ones[1]      1.00     NaN 0.00   1.00  1.00  1.00  1.00  1.00   NaN  NaN
#> vec_ones[2]      1.00     NaN 0.00   1.00  1.00  1.00  1.00  1.00   NaN  NaN
#> lp__            -8.88    0.11 1.60 -12.56 -9.72 -8.66 -7.61 -6.65   224    1
#> 
#> Samples were drawn using NUTS(diag_e) at Fri Jan  9 01:19:53 2026.
#> 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).
#> 
#> $stan_list
#> $stan_list$omega
#> [1] 0.5
#> 
#> $stan_list$delta_P
#> [1] -0.3566749
#> 
#> $stan_list$sigma_P1
#> [1] 0.1533197
#> 
#> $stan_list$sigma_P2
#> [1] 0.1533197
#> 
#> $stan_list$tau_sd2
#> [1] 0.09266264
#> 
#> $stan_list$tau_sd3
#> [1] 0.04633132
#> 
#> $stan_list$J
#> [1] 2
#> 
#> $stan_list$Sigma
#>             [,1]        [,2]
#> [1,] 0.005405405 0.004273504
#> [2,] 0.004273504 0.004273504
#> 
#> $stan_list$theta_hat
#> [1] -0.1278334
#> 
#> $stan_list$theta_hat_sd
#> [1] 0.08926063
#> 
#> 
#> $stan_file
#> [1] "phase23_interim_pfs.stan"
#>