tsegestsim: Simulate Survival Data for Two-Stage Estimation Method Using...

In trtswitch: Treatment Switching

Simulate Survival Data for Two-Stage Estimation Method Using g-estimation

Description

Obtains the simulated data for baseline prognosis, disease progression, treatment switching, death, and time-dependent covariates.

Usage

tsegestsim(
  n = 500L,
  allocation1 = 2L,
  allocation2 = 1L,
  pbprog = 0.5,
  trtlghr = -0.5,
  bprogsl = 0.3,
  shape1 = 1.8,
  scale1 = 2.5e-05,
  shape2 = 1.7,
  scale2 = 1.5e-05,
  pmix = 0.5,
  admin = 5000,
  pcatnotrtbprog = 0.5,
  pcattrtbprog = 0.25,
  pcatnotrt = 0.2,
  pcattrt = 0.1,
  catmult = 0.5,
  tdxo = 1,
  ppoor = 0.1,
  pgood = 0.04,
  ppoormet = 0.4,
  pgoodmet = 0.2,
  xomult = 1.4188308,
  milestone = 546,
  swtrt_control_only = 1L,
  outputRawDataset = 1L,
  seed = NA_integer_
)

Arguments

n	The total sample size for two treatment arms combined.
allocation1	The number of subjects in the active treatment group in a randomization block.
allocation2	The number of subjects in the control group in a randomization block.
pbprog	The probability of having poor prognosis at baseline.
trtlghr	The treatment effect in terms of log hazard ratio.
bprogsl	The poor prognosis effect in terms of log hazard ratio.
shape1	The shape parameter for the Weibull event distribution for the first component.
scale1	The scale parameter for the Weibull event distribution for the first component.
shape2	The shape parameter for the Weibull event distribution for the second component.
scale2	The scale parameter for the Weibull event distribution for the second component.
pmix	The mixing probability of the first component Weibull distribution.
admin	The administrative censoring time.
pcatnotrtbprog	The probability of developing metastatic disease on control treatment with poor baseline prognosis.
pcattrtbprog	The probability of developing metastatic disease on active treatment with poor baseline prognosis.
pcatnotrt	The probability of developing metastatic disease on control treatment with good baseline prognosis.
pcattrt	The probability of developing metastatic disease on active treatment with good baseline prognosis.
catmult	The impact of metastatic disease on shortening remaining survival time.
tdxo	Whether treatment crossover depends on time-dependent covariates between disease progression and treatment switching.
ppoor	The probability of switching for poor baseline prognosis with no metastatic disease.
pgood	The probability of switching for good baseline prognosis with no metastatic disease.
ppoormet	The probability of switching for poor baseline prognosis after developing metastatic disease.
pgoodmet	The probability of switching for good baseline prognosis after developing metastatic disease.
xomult	The direct effect of crossover on extending remaining survival time.
milestone	The milestone to calculate restricted mean survival time.
swtrt_control_only	Whether treatment switching occurred only in the control group.
outputRawDataset	Whether to output the raw data set.
seed	The seed to reproduce the simulation results. The seed from the environment will be used if left unspecified.

Value

A list with two data frames.

• sumdata: A data frame with the following variables:

  • simtrueconstmean: The true control group restricted mean survival time (RMST).

  • simtrueconstlb: The lower bound for control group RMST.

  • simtrueconstub: The upper bound for control group RMST.

  • simtrueconstse: The standard error for control group RMST.

  • simtrueexpstmean: The true experimental group restricted mean survival time (RMST).

  • simtrueexpstlb: The lower bound for experimental group RMST.

  • simtrueexpstub: The upper bound for experimental group RMST.

  • simtrueexpstse: The standard error for experimental group RMST.

  • simtrue_coxwbprog_hr: The treatment hazard ratio from the Cox model adjusting for baseline prognosis.

  • simtrue_cox_hr: The treatment hazard ratio from the Cox model without adjusting for baseline prognosis.

• paneldata: A counting process style data frame with the following variables:

  • id: The subject ID.

  • trtrand: The randomized treatment arm.

  • bprog: Whether the patient had poor baseline prognosis.

  • tstart: The left end of time interval.

  • tstop: The right end of time interval.

  • died: Whether the patient died.

  • progressed: Whether the patient had disease progression.

  • timePFSobs: The observed time of disease progression at regular scheduled visits.

  • progtdc: The time-dependent covariate for progression.

  • catevent: Whether the patient developed metastatic disease.

  • cattime: When the patient developed metastatic disease.

  • cattdc: The time-dependent covariate for cat event.

  • catlag: The lagged value of cattdc.

  • xo: Whether the patient switched treatment.

  • xotime: When the patient switched treatment.

  • xotdc: The time-dependent covariate for treatment switching.

  • xotime_upper: The upper bound of treatment switching time.

  • censor_time: The administrative censoring time.

Author(s)

Kaifeng Lu, [e-mail]

Examples

sim1 <- tsegestsim(
  n = 500, allocation1 = 2, allocation2 = 1, pbprog = 0.5, 
  trtlghr = -0.5, bprogsl = 0.3, shape1 = 1.8, 
  scale1 = 0.000025, shape2 = 1.7, scale2 = 0.000015, 
  pmix = 0.5, admin = 5000, pcatnotrtbprog = 0.5, 
  pcattrtbprog = 0.25, pcatnotrt = 0.2, pcattrt = 0.1, 
  catmult = 0.5, tdxo = 1, ppoor = 0.1, pgood = 0.04, 
  ppoormet = 0.4, pgoodmet = 0.2, xomult = 1.4188308, 
  milestone = 546, outputRawDataset = 1, seed = 2000)

trtswitch documentation built on Nov. 2, 2024, 1:07 a.m.