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,
  allocation1 = 1L,
  allocation2 = 1L,
  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_,
  accrualTime = 0L,
  accrualIntensity = NA_real_,
  followupTime = NA_real_,
  fixedFollowup = 0L,
  plannedTime = NA_real_,
  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.
allocation1	Number of subjects in the active treatment group in a randomization block. Defaults to 1 for equal randomization.
allocation2	Number of subjects in the control group in a randomization block. Defaults to 1 for equal randomization.
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 (Llag) 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 for the experimental arm.
theta1_0	Negative log time ratio for A for the control arm.
theta2	Negative log time ratio for L.
rate_C	Hazard rate for random (dropout) censoring.
accrualTime	A vector that specifies the starting time of piecewise Poisson enrollment time intervals. Must start with 0, e.g., c(0, 3) breaks the time axis into 2 accrual intervals: [0, 3) and [3, Inf).
accrualIntensity	A vector of accrual intensities. One for each accrual time interval.
followupTime	Follow-up time for a fixed follow-up design.
fixedFollowup	Whether a fixed follow-up design is used. Defaults to 0 for variable follow-up.
plannedTime	The calendar time for the analysis.
days	Number of days in each treatment cycle.
n	Number of subjects per simulation.
NSim	Number of simulated datasets.
seed	Random seed for reproducibility.

Details

The simulation algorithm is adapted from Xu et al. (2022) and simulates disease progression status while incorporating the multiplicative effects of both baseline and time-dependent covariates on survival time. The design options tdxo and coxo specify the timing of treatment switching and the study arm eligibility for switching, respectively. Time is measured in days.

In a fixed follow-up design, all subjects share the same follow-up duration. In contrast, under a variable follow-up design, follow-up time also depends on each subject's enrollment date. The number of treatment cycles for a subject is determined by dividing the follow-up time by the number of days in each cycle.

1. At randomization, subjects are assigned to treatment arms using block randomization, with allocation1 patients assigned to active treatment and allocation2 to control within each randomized block. A baseline covariate is also generated for each subject:

   X_i \sim \mbox{Bernoulli}(p_{X_1} R_i + p_{X_0} (1-R_i))

2. The initial survival time is drawn from an exponential distribution with hazard:

   rate_T \exp(\beta_1 R_i + \beta_2 X_i)

   We define the event indicator at cycle j as

   Y_{i,j} = I(T_i \leq j\times days)

3. The initial states are set to L_{i,0} = 0, Z_{i,0} = 0, A_{i,0} = 0, Y_{i,0} = 0. For each treatment cycle j=1,\ldots,J, we set tstart = (j-1) \times days.

4. Generate time-dependent covariates:

   \mbox{logit} P(L_{i,j}=1|\mbox{history}) = \gamma_0 + \gamma_1 A_{i,j-1} + \gamma_2 L_{i,j-1} + \gamma_3 X_i + \gamma_4 R_i

5. If T_i \leq j \times days, set tstop = T_i and Y_{i,j} = 1, which completes data generation for subject i.

6. If T_i > j \times days, set tstop = j\times days, Y_{i,j} = 0, and perform the following before proceeding to the next cycle for the subject.

7. Generate disease progression status: If Z_{i,j-1} = 0,

   \mbox{logit} P(Z_{i,j}=1 | \mbox{history}) = \zeta_0 + \zeta_1 L_{i,j} + \zeta_2 X_i + \zeta_3 R_i

   Otherwise, set Z_{i,j} = 1.

8. Generate alternative therapy status: If A_{i,j-1} = 0, determine switching eligibility based on design options. If switching is allowed:

   \mbox{logit} P(A_{i,j} = 1 | \mbox{history}) = \alpha_0 + \alpha_1 L_{i,j} + \alpha_2 X_i

   If switching is now allowed, set A_{i,j} = 0. If A_{i,j-1} = 1, set A_{i,j} = 1 (once switched to alternative therapy, remain on alternative therapy).

9. Update survival time based on changes in alternative therapy status and time-dependent covariates:

   T_i = j\times days + (T_i - j\times days) \exp\{ -(\theta_{1,1}R_i + \theta_{1,0}(1-R_i))(A_{i,j} - A_{i,j-1}) -\theta_2 (L_{i,j} - L_{i,j-1})\}

Additional random censoring times are generated from an exponential distribution with hazard rate rate_C.

An extra record is generated when the minimum of the latent survival time, the random censoring time, and the administrative censoring time is greater than the number of regular treatment cycles times days per cycle.

Finally we apply the lag function so that Z_{i,j} and A_{i,j} represent the PD status and alternative therapy status at the start of cycle j (and thus remain appplicable for the entire cycle j) for subject i.

To estimate the true treatment effect in a Cox marginal structural model, one can set \alpha_0 = -\infty, which effectively forces A_{i,j} = 0 (disabling treatment switching). The coefficient for the randomized treatment can then be estimated using a Cox proportional hazards model.

Value

A list of data frames, each containing simulated longitudinal covariate, pd status, alternative therapy status, and event history data with the following variables:

• id: Subject identifier.

• arrivalTime: The enrollment time for the subject.

• 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 at tstart predicting survival and switching; affected by treatment switching.

• Llag: Lagged value of L.

• Z: Disease progression status at tstart.

• A: Treatment switching status at tstart.

• Alag: Lagged value of A.

• event: 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

library(dplyr)

simulated.data <- tssim(
  tdxo = 1, coxo = 1, allocation1 = 1, allocation2 = 1,
  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, accrualIntensity = 20/30, 
  fixedFollowup = FALSE, plannedTime = 1350, days = 30,
  n = 500, NSim = 100, seed = 314159)
  
simulated.data[[1]] %>% filter(id == 1)

trtswitch documentation built on Nov. 5, 2025, 7:08 p.m.