Skip to contents

PoS estimation

Source: R/gen_pos.R
gen_pos.Rd

Computes the predicted probability of success (PoS) for a phase III clinical trial by integrating early-phase efficacy data – objective response rate (ORR) and/or progression-free survival (PFS) – with prior beliefs about study success. The prior distribution for the benchmark PoS is specified as a Beta distribution, parameterized by its mean and variance. Treatment effect estimation for ORR supports both two-arm and single-arm designs, with the latter incorporating historical control information.

Usage

gen_pos(
  target_hr,
  J,
  nevents3,
  hr_bound,
  omega_mean = 0.52,
  omega_var = 0.02,
  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,
  low_soc_rr,
  upp_soc_rr,
  ci_rr = 0.8,
  use_orr = FALSE,
  single_arm = FALSE,
  use_pfs = FALSE,
  het_degree_p2 = "small",
  het_degree_p3 = "very small",
  ratio = 1,
  indication = 6,
  m_0 = NA,
  m_1 = NA,
  nu_0 = NA,
  nu_1 = NA,
  lm_sd = NA,
  niter = 1000,
  nchains = 1,
  ncores = 1,
  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_mean

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

omega_var

Variance of the Beta prior for omega.

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

low_soc_rr

Lower bound of historical control response rate for single-arm estimation.

upp_soc_rr

Upper bound of historical control response rate for single-arm estimation.

ci_rr

Confidence level for control response rate bounds, Default: 0.8.

use_orr

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

single_arm

whether ORR data is from a single-arm trial, 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

indication

Integer from 1 to 6 for selecting indication-specific ORR-PFS regression parameters, as follows: 1 = hematologic malignancies 2 = gynecologic cancers 3 = thoracic cancers 4 = other solid tumors 5 = breast cancer 6 = any tumor type Default: 6

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. Auto-filled based on indication if not supplied.

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. Auto-filled based on indication if not supplied.

nu_0

standard error of m_0. A value is expected only when use_orr = TRUE and use_pfs = TRUE. Auto-filled based on indication if not supplied.

nu_1

standard error of m_1. A value is expected only when use_orr = TRUE and use_pfs = TRUE. Auto-filled based on indication if not supplied.

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. Auto-filled based on indication if not supplied.

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, the corresponding standard errors for each analysis, and the posterior mean and variance for omega. 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.

See also

gather_draws mutate, summarise, group_by map2, reexports MCMC-traces, MCMC-diagnostics

Examples

# Using both ORR and PFS from a prior single-arm study with a Beta prior on 
# omega

gen_pos(
  target_hr = 0.70,
  J = 2,
  nevents3 = c(370, 468),
  hr_bound = c(0.7790, 0.8204),
  thres = 0.01,
  omega_mean = 0.3,
  omega_var = 0.03,
  est_obs_pfs = 0.73,
  low_obs_pfs = 0.61,
  upp_obs_pfs = 0.91,
  use_pfs = TRUE,
  n_trt2 = 100,
  n_resp_trt2 = 40,
  low_soc_rr = 0.05,
  upp_soc_rr = 0.2,
  use_orr = TRUE,
  single_arm = TRUE,
  ncores = 1,
  nchains = 1,
  seed = 222
)
#> 
#> 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: 0.015 seconds (Warm-up)
#> Chain 1:                0.011 seconds (Sampling)
#> Chain 1:                0.026 seconds (Total)
#> Chain 1: 
#> Warning: There were 1 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> # A tibble: 2 × 5
#>       J   pos pos_se omega_mean omega_var
#>   <int> <dbl>  <dbl>      <dbl>     <dbl>
#> 1     1 0.634 0.0215      0.350    0.0304
#> 2     2 0.748 0.0194      0.350    0.0304