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Simulate Survival Data for Two-Stage Estimation with g-estimation

Source: R/RcppExports.R
tsegestsim.Rd

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 = 360,
  shape2 = 1.7,
  scale2 = 688,
  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,
  seed = 0L
)

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.

seed

The seed to reproduce the simulation results.

Value

A list with two data frames.

  • sumdata: A summary 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.

    • simtrue_aftwbprog_af: The average acceleration factor from the Weibull AFT model adjusting for baseline prognosis.

    • simtrue_aft_af: The average acceleration factor from the Weibull AFT model without adjusting for baseline prognosis.

  • paneldata: A counting process style subject-level 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.

    • event: Whether the patient died at the end of the interval.

    • timeOS: The observed survival time.

    • died: Whether the patient died during the study.

    • 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.

    • xo: Whether the patient switched treatment.

    • xotime: When the patient switched treatment.

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

    • censor_time: The administrative censoring time.

References

NR Latimer, IR White, K Tilling, and U Siebert. Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding. Statistical Methods in Medical Research. 2020;29(10):2900-2918.

Author

Kaifeng Lu, kaifenglu@gmail.com

Examples


sim1 <- tsegestsim(
  n = 500, allocation1 = 2, allocation2 = 1, pbprog = 0.5, 
  trtlghr = -0.5, bprogsl = 0.3, shape1 = 1.8, 
  scale1 = 360, shape2 = 1.7, scale2 = 688, 
  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, seed = 2000)