SimNPH (version 0.5.7)

generate_delayed_effect: Generate Dataset with delayed effect

Description

Generate Dataset with delayed effect

Create an empty assumtions data.frame for generate_delayed_effect

Calculate hr after onset of treatment effect

Calculate true summary statistics for scenarios with delayed treatment effect

Usage

generate_delayed_effect(condition, fixed_objects = NULL)
assumptions_delayed_effect(print = interactive())
hr_after_onset_from_PH_effect_size(
  design,
  target_power_ph = NA_real_,
  final_events = NA_real_,
  target_alpha = 0.025
)
cen_rate_from_cen_prop_delayed_effect(design)
true_summary_statistics_delayed_effect(
  Design,
  cutoff_stats = NULL,
  milestones = NULL,
  fixed_objects = NULL
)

Value

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

For assumptions_delayed_effect: a design tibble with default values invisibly

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

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

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

Arguments

condition

condition row of Design dataset

fixed_objects

additional settings, see details

print

print code to generate parameter set?

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

Design

Design data.frame for delayed effect

cutoff_stats

(optionally named) cutoff times, see details

milestones

(optionally named) vector of times at which milestone survival should be calculated

Functions

• generate_delayed_effect(): simulates a dataset with delayed treatment effect

• assumptions_delayed_effect(): generate default assumptions data.frame

• hr_after_onset_from_PH_effect_size(): Calculate hr after onset of treatment effect of the hazards from PH effect size

• cen_rate_from_cen_prop_delayed_effect(): calculate censoring rate from censoring proportion

• true_summary_statistics_delayed_effect(): calculate true summary statistics for delayed effect

Details

Condidtion has to contain the following columns:

• n_trt number of paitents in treatment arm

• n_ctrl number of patients in control arm

• delay time until onset of effect

• hazard_ctrl hazard in the control arm = hazard before onset of treatment effect

• hazard_trt hazard in the treatment arm afert onset of treatment effect

If fixed_objects is given and contains an element t_max, then this is used as the cutoff for the simulation used internally. If t_max is not given in this way the 1-(1/10000) quantile of the survival distribution in the control or treatment arm is used (which ever is larger).

assumptions_delayed_effect generates a default design data.frame for use with generate_delayed_effect. 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.)

hr_after_onset_from_PH_effect_size calculates the hazard ratio after onset of treatment effect as follows: First, the hazard ratio needed to archive the desired power under proportional hazards is calculated by inverting Schönfeld's sample size formula. Second the median survival times for both arm under this hazard ratio and proportional hazards are calculated. Finally the hazard rate of the treatment arm after onset of treatment effect is set such that the median survival time is the same as the one calculated under proportional hazards.

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

cen_rate_from_cen_prop_delayed_effect 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.

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.

Examples

one_simulation <- merge(
    assumptions_delayed_effect(),
    design_fixed_followup(),
    by=NULL
  ) |>
  head(1) |>
  generate_delayed_effect()
head(one_simulation)
tail(one_simulation)
Design <- assumptions_delayed_effect()
Design
my_design <- merge(
  assumptions_delayed_effect(),
  design_fixed_followup(),
  by=NULL
)

my_design$hazard_ctrl <- 0.05
my_design$final_events <- ceiling((my_design$n_trt + my_design$n_ctrl)*0.75)
my_design$hazard_trt <- NA
my_design <- hr_after_onset_from_PH_effect_size(my_design, target_power_ph=0.9)
my_design
design <- expand.grid(
  delay=seq(0, 10, by=5),            # delay of 0, 1, ..., 10 days
  hazard_ctrl=0.2,                   # hazard under control and before treatment effect
  hazard_trt=0.02,                   # hazard after onset of treatment effect
  censoring_prop=c(0.1, 0.25, 0.01), # 10%, 25%, 1% random censoring
  followup=100,                      # followup of 100 days
  n_trt=50,                          # 50 patients treatment
  n_ctrl=50                          # 50 patients control
)
cen_rate_from_cen_prop_delayed_effect(design)
my_design <- merge(
    assumptions_delayed_effect(),
    design_fixed_followup(),
    by=NULL
  )
my_design <- true_summary_statistics_delayed_effect(my_design)
my_design