tssim: Simulate Data for Treatment Switching

In trtswitch: Treatment Switching

Simulate Data for Treatment Switching

Description

Simulates data for studies involving treatment switching, incorporating time-dependent confounding. The generated data can be used to evaluate methods for handling treatment switching in survival analysis.

Usage

tssim(
  tdxo = 0L,
  coxo = 1L,
  p_R = 0.5,
  p_X_1 = NA_real_,
  p_X_0 = NA_real_,
  rate_T = NA_real_,
  beta1 = NA_real_,
  beta2 = NA_real_,
  gamma0 = NA_real_,
  gamma1 = NA_real_,
  gamma2 = NA_real_,
  gamma3 = NA_real_,
  gamma4 = NA_real_,
  zeta0 = NA_real_,
  zeta1 = NA_real_,
  zeta2 = NA_real_,
  zeta3 = NA_real_,
  alpha0 = NA_real_,
  alpha1 = NA_real_,
  alpha2 = NA_real_,
  theta1_1 = NA_real_,
  theta1_0 = NA_real_,
  theta2 = NA_real_,
  rate_C = NA_real_,
  followup = NA_integer_,
  days = NA_integer_,
  n = NA_integer_,
  NSim = 1000L,
  seed = NA_integer_
)

Arguments

tdxo	Logical indicator for timing of treatment switching: 1: Treatment switching can occur at or after disease progression. 0: Treatment switching is restricted to the time of disease progression.
coxo	Logical indicator for arm-specific treatment switching: 1: Treatment switching occurs only in the control arm. 0: Treatment switching is allowed in both arms.
p_R	Probability of randomization to the experimental arm.
p_X_1	Probability of poor baseline prognosis in the experimental arm.
p_X_0	Probability of poor baseline prognosis in the control arm.
rate_T	Baseline hazard rate for time to death.
beta1	Log hazard ratio for randomized treatment (R).
beta2	Log hazard ratio for baseline covariate (X).
gamma0	Intercept for the time-dependent covariate model (L).
gamma1	Coefficient for lagged treatment switching (Alag) in the L model.
gamma2	Coefficient for lagged L in the L model.
gamma3	Coefficient for baseline covariate (X) in the L model.
gamma4	Coefficient for randomized treatment (R) in the L model.
zeta0	Intercept for the disease progression model (Z).
zeta1	Coefficient for L in the Z model.
zeta2	Coefficient for baseline covariate (X) in the Z model.
zeta3	Coefficient for randomized treatment (R) in the Z model.
alpha0	Intercept for the treatment switching model (A).
alpha1	Coefficient for L in the A model.
alpha2	Coefficient for baseline covariate (X) in the A model.
theta1_1	Negative log time ratio for A (experimental arm).
theta1_0	Negative log time ratio for A (control arm).
theta2	Negative log time ratio for L.
rate_C	Hazard rate for random (dropout) censoring.
followup	Number of treatment cycles per subject.
days	Number of days in each treatment cycle.
n	Number of subjects per simulation.
NSim	Number of simulated datasets.
seed	Random seed for reproducibility.

Value

A list of data frames, each containing simulated longitudinal and event history data with the following variables:

• id: Subject identifier.

• trtrand: Randomized treatment assignment (0 = control, 1 = experimental)

• bprog: Baseline prognosis (0 = good, 1 = poor).

• tpoint: Treatment cycle index.

• tstart: Start day of the treatment cycle.

• tstop: End day of the treatment cycle.

• L: Time-dependent covariate predicting survival and switching; affected by treatment switching.

• Llag: Lagged value of L.

• Z: Disease progression status at tstop.

• A: Treatment switching status at tstop.

• Alag: Lagged value of A.

• Y: Death indicator at tstop.

• timeOS: Observed time to death or censoring.

• died: Indicator of death by end of follow-up.

• progressed: Indicator of disease progression by end of follow-up.

• timePD: Observed time to progression or censoring.

• xo: Indicator for whether treatment switching occurred.

• xotime: Time of treatment switching (if applicable).

• censor_time: Administrative censoring time.

Author(s)

Kaifeng Lu, [e-mail]

References

Jessica G. Young, and Eric J. Tchetgen Tchetgen. Simulation from a known Cox MSM using standard parametric models for the g-formula. Statistics in Medicine. 2014;33(6):1001-1014.

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.

Jing Xu, Guohui Liu, and Bingxia Wang. Bias and type I error control in correcting treatment effect for treatment switching using marginal structural models in Phse III oncology trials. Journal of Biopharmaceutical Statistics. 2022;32(6):897-914.

Examples

simulated.data <- tssim(
  tdxo = 0, coxo = 0, p_R = 0.5, p_X_1 = 0.3, p_X_0 = 0.3, 
  rate_T = 0.002, beta1 = -0.5, beta2 = 0.3, 
  gamma0 = 0.3, gamma1 = -0.9, gamma2 = 0.7, gamma3 = 1.1, gamma4 = -0.8,
  zeta0 = -3.5, zeta1 = 0.5, zeta2 = 0.2, zeta3 = -0.4, 
  alpha0 = 0.5, alpha1 = 0.5, alpha2 = 0.4, 
  theta1_1 = -0.4, theta1_0 = -0.4, theta2 = 0.2,
  rate_C = 0.0000855, followup = 20, days = 30,
  n = 500, NSim = 100, seed = 314159)

trtswitch documentation built on June 8, 2025, 1:45 p.m.