Create an empty assumtions data.frame for generate_progression

Generate Dataset with changing hazards after disease progression

Calculate progression rate from proportion of patients who progress

Calculate hr after onset of treatment effect

Usage

assumptions_progression(print = interactive())

generate_progression(condition, fixed_objects = NULL)

true_summary_statistics_progression(
  Design,
  what = "os",
  cutoff_stats = NULL,
  fixed_objects = NULL,
  milestones = NULL
)

progression_rate_from_progression_prop(design)

cen_rate_from_cen_prop_progression(design)

hazard_before_progression_from_PH_effect_size(
  design,
  target_power_ph = NA_real_,
  final_events = NA_real_,
  target_alpha = 0.025
)

Arguments

print: print code to generate parameter set?
condition: condition row of Design dataset
fixed_objects: additional settings, see details
Design: Design data.frame for subgroup
what: True summary statistics for which estimand
cutoff_stats: (optionally named) cutoff time, see details
milestones: (optionally named) vector of times at which milestone survival should be calculated
design: design data.frame
target_power_ph: target power under proportional hazards
final_events: target events for inversion of Schönfeld Formula, defaults to condition$final_events
target_alpha: target one-sided alpha level for the power calculation

Value

For generate_progression: a design tibble with default values invisibly

For generate_progression: A dataset with the columns t (time) and trt (1=treatment, 0=control), evt (event, currently TRUE for all observations), t_ice (time of intercurrent event), ice (intercurrent event)

For true_summary_statistics_subgroup: the design data.frame passed as argument with the additional columns

For progression_rate_from_progression_prop: the design data.frame passed as argument with the additional columns prog_rate_trt, prog_rate_ctrl

for cen_rate_from_cen_prop_progression: design data.frame with the additional column random_withdrawal

For hazard_before_progression_from_PH_effect_size: the design data.frame passed as argument with the additional column hazard_trt.

Details

assumptions_progression generates a default design data.frame for use with generate_progression If print is TRUE code to produce the template is also printed for copying, pasting and editing by the user. (This is the default when run in an interactive session.)

Condidtion has to contain the following columns:

n_trt number of paitents in treatment arm
n_ctrl number of patients in control arm
hazard_ctrl hazard in the control arm
hazard_trt hazard in the treatment arm for not cured patients
hazard_after_prog hazard after disease progression
prog_rate_ctrl hazard rate for disease progression unter control
prog_rate_trt hazard rate for disease progression unter treatment

what can be "os" for overall survival and "pfs" for progression free survival.

The if fixed_objects contains t_max then this value is used as the maximum time to calculate function like survival, hazard, ... of the data generating models. If this is not given t_max is choosen as the minimum of the 1-(1/10000) quantile of all survival distributions in the model.

cutoff_stats are the times used to calculate the statistics like average hazard ratios and RMST, that are only calculated up to a certain point.

For progression_rate_from_progression_prop, the design data.frame, has to contain the columns prog_prop_trt and prog_prop_ctrl with the proportions of patients, who progress in the respective arms.

cen_rate_from_cen_prop_progression takes the proportion of censored patients from the column censoring_prop. This column describes the proportion of patients who are censored randomly before experiencing an event, without regard to administrative censoring.

hazard_before_progression_from_PH_effect_size calculates the hazard ratio after onset of treatment effect as follows: First calculate the hazard in the control arm that would give the same median survival under an exponential model. Then calculate the median survival in the treatment arm that would give the desired power of the logrank test under exponential models in control and treatment arm. Then callibrate the hazard before progression in the treatment arm to give the same median survival time.

This is a heuristic and to some extent arbitrary approach to calculate hazard ratios that correspond to reasonable and realistic scenarios.

Functions

assumptions_progression(): generate default assumptions data.frame
generate_progression(): simulates a dataset with changing hazards after disease progression
true_summary_statistics_progression(): calculate true summary statistics for scenarios with disease progression
progression_rate_from_progression_prop(): Calculate progression rate from proportion of patients who progress
cen_rate_from_cen_prop_progression(): calculate censoring rate from censoring proportion
hazard_before_progression_from_PH_effect_size(): Calculate hazard in the treatment arm before progression from PH effect size

Examples

Design <- assumptions_progression()
Design
#>    hazard_ctrl   hazard_trt hazard_after_prog prog_rate_ctrl prog_rate_trt
#> 1 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001897734
#> 2 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001423300
#> 3 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001265156
#>   random_withdrawal
#> 1      0.0001897734
#> 2      0.0001897734
#> 3      0.0001897734
one_simulation <- merge(
    assumptions_progression(),
    design_fixed_followup(),
    by=NULL
  ) |>
  tail(1) |>
  generate_progression()
head(one_simulation)
#>     t trt  evt t_ice  ice
#> 1 531   1 TRUE   334 TRUE
#> 2 700   1 TRUE   678 TRUE
#> 3 172   1 TRUE     4 TRUE
#> 4 556   1 TRUE   173 TRUE
#> 5 553   1 TRUE   330 TRUE
#> 6 201   1 TRUE    77 TRUE
tail(one_simulation)
#>        t trt  evt t_ice   ice
#> 295  344   0 TRUE   Inf FALSE
#> 296 1330   0 TRUE   519  TRUE
#> 297  243   0 TRUE   Inf FALSE
#> 298   73   0 TRUE   Inf FALSE
#> 299  134   0 TRUE    37  TRUE
#> 300  124   0 TRUE   115  TRUE

my_design <- merge(
  assumptions_progression(),
  design_fixed_followup(),
  by=NULL
)

my_design_os  <- true_summary_statistics_progression(my_design, "os")
my_design_pfs <- true_summary_statistics_progression(my_design, "pfs")
my_design_os
#>    hazard_ctrl   hazard_trt hazard_after_prog prog_rate_ctrl prog_rate_trt
#> 1 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001897734
#> 2 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001423300
#> 3 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001265156
#>   random_withdrawal n_trt n_ctrl followup recruitment median_survival_trt
#> 1      0.0001897734   150    150    730.5     182.625            479.4919
#> 2      0.0001897734   150    150    730.5     182.625            532.4814
#> 3      0.0001897734   150    150    730.5     182.625            555.2914
#>   median_survival_ctrl
#> 1             421.3531
#> 2             421.3531
#> 3             421.3531
my_design_pfs
#>    hazard_ctrl   hazard_trt hazard_after_prog prog_rate_ctrl prog_rate_trt
#> 1 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001897734
#> 2 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001423300
#> 3 0.0009488668 0.0006325779       0.003795467    0.001897734   0.001265156
#>   random_withdrawal n_trt n_ctrl followup recruitment median_survival_trt
#> 1      0.0001897734   150    150    730.5     182.625            273.9375
#> 2      0.0001897734   150    150    730.5     182.625            337.1538
#> 3      0.0001897734   150    150    730.5     182.625            365.2500
#>   median_survival_ctrl
#> 1                243.5
#> 2                243.5
#> 3                243.5
my_design <- merge(
    assumptions_progression(),
    design_fixed_followup(),
    by=NULL
  )
my_design$prog_rate_ctrl <- NA_real_
my_design$prog_rate_trt <- NA_real_
my_design$prog_prop_trt <- 0.2
my_design$prog_prop_ctrl <- 0.3
my_design <- progression_rate_from_progression_prop(my_design)
my_design
#>    hazard_ctrl   hazard_trt hazard_after_prog prog_rate_ctrl prog_rate_trt
#> 1 0.0009488668 0.0006325779       0.003795467   0.0004066572  0.0001581445
#> 2 0.0009488668 0.0006325779       0.003795467   0.0004066572  0.0001581445
#> 3 0.0009488668 0.0006325779       0.003795467   0.0004066572  0.0001581445
#>   random_withdrawal n_trt n_ctrl followup recruitment prog_prop_trt
#> 1      0.0001897734   150    150    730.5     182.625           0.2
#> 2      0.0001897734   150    150    730.5     182.625           0.2
#> 3      0.0001897734   150    150    730.5     182.625           0.2
#>   prog_prop_ctrl
#> 1            0.3
#> 2            0.3
#> 3            0.3
design <- expand.grid(
hazard_ctrl         = m2r(15),          # hazard under control
hazard_trt          = m2r(18),          # hazard under treatment
hazard_after_prog   = m2r(3),           # hazard after progression
prog_rate_ctrl      = m2r(12),          # hazard for disease progression under control
prog_rate_trt       = m2r(c(12,16,18)), # hazard for disease progression under treatment
censoring_prop      = 0.1,              # rate of random withdrawal
followup            = 100,              # follow up time
n_trt               = 50,               # patients in treatment arm
n_ctrl              = 50                # patients in control arm
)
cen_rate_from_cen_prop_progression(design)
#>   hazard_ctrl  hazard_trt hazard_after_prog prog_rate_ctrl prog_rate_trt
#> 1 0.001518187 0.001265156       0.007590934    0.001897734   0.001897734
#> 2 0.001518187 0.001265156       0.007590934    0.001897734   0.001423300
#> 3 0.001518187 0.001265156       0.007590934    0.001897734   0.001265156
#>   censoring_prop followup n_trt n_ctrl random_withdrawal
#> 1            0.1      100    50     50      0.0003575638
#> 2            0.1      100    50     50      0.0003285652
#> 3            0.1      100    50     50      0.0003179468
# \donttest{
my_design <- merge(
  design_fixed_followup(),
  assumptions_progression(),
  by=NULL
)

my_design$hazard_trt <- NULL
my_design$final_events <- ceiling(0.75 * (my_design$n_trt + my_design$n_ctrl))

my_design <- hazard_before_progression_from_PH_effect_size(my_design, target_power_ph=0.7)
my_design
#>   n_trt n_ctrl followup recruitment  hazard_ctrl hazard_after_prog
#> 1   150    150    730.5     182.625 0.0009488668       0.003795467
#> 2   150    150    730.5     182.625 0.0009488668       0.003795467
#> 3   150    150    730.5     182.625 0.0009488668       0.003795467
#>   prog_rate_ctrl prog_rate_trt random_withdrawal final_events   hazard_trt
#> 1    0.001897734   0.001897734      0.0001897734          225 0.0001912378
#> 2    0.001897734   0.001423300      0.0001897734          225 0.0004496909
#> 3    0.001897734   0.001265156      0.0001897734          225 0.0005343131
# }