assumptions_progression {SimNPH}R Documentation

Create an empty assumtions data.frame for generate_progression

Description

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

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:

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.

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.

Functions

Examples

Design <- assumptions_progression()
Design
one_simulation <- merge(
    assumptions_progression(),
    design_fixed_followup(),
    by=NULL
  ) |>
  tail(1) |>
  generate_progression()
head(one_simulation)
tail(one_simulation)

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
my_design_pfs
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
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)

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
[Package SimNPH version 0.5.5 Index]