PoS estimation

Usage

gen_pos(
  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,
  plots_out = FALSE,
  ...
)

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
plots_out: whether plots for MCMC chains and autocorrelation should be printed, Default: FALSE
...: params to pass to stan run

Value

tibble of PoS estimates and the corresponding standard errors for each analysis. If plots_out is turned on, then the MCMC chains mixing and autocorrelation plots are provided as well.

Specification

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

Examples

# use PFS data from a prior study

  gen_pos(
    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 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 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: 0.016 seconds (Warm-up)
#> Chain 1:                0.008 seconds (Sampling)
#> Chain 1:                0.024 seconds (Total)
#> Chain 1: 
#> # A tibble: 2 × 3
#>       J   pos pos_se
#>   <int> <dbl>  <dbl>
#> 1     1 0.202 0.0180
#> 2     2 0.322 0.0209