tsegestsim function

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

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

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.

Returns

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.

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)

Author(s)

Kaifeng Lu, kaifenglu@gmail.com