trtswitch (version 0.2.2)

ipcw: Inverse Probability of Censoring Weights (IPCW) for Treatment Switching

Description

Excludes data after treatment switching and fits a switching model to estimate the probability of not switching. The inverse of these probabilities (inverse probability of censoring weights) are then used as weights in a weighted Cox model to obtain the adjusted hazard ratio.

Usage

ipcw(
  data,
  id = "id",
  stratum = "",
  tstart = "tstart",
  tstop = "tstop",
  event = "event",
  treat = "treat",
  swtrt = "swtrt",
  swtrt_time = "swtrt_time",
  base_cov = "",
  numerator = "",
  denominator = "",
  logistic_switching_model = FALSE,
  strata_main_effect_only = TRUE,
  ns_df = 3,
  firth = FALSE,
  flic = FALSE,
  stabilized_weights = TRUE,
  trunc = 0,
  trunc_upper_only = TRUE,
  swtrt_control_only = TRUE,
  alpha = 0.05,
  ties = "efron",
  boot = FALSE,
  n_boot = 1000,
  seed = NA
)

Value

A list with the following components:

• logrank_pvalue: The two-sided p-value of the log-rank test for the ITT analysis.

• cox_pvalue: The two-sided p-value for treatment effect based on the weighted Cox model excluding data after treatment switch. If boot is TRUE, this value represents the bootstrap p-value.

• hr: The estimated hazard ratio from the Cox model.

• hr_CI: The confidence interval for hazard ratio.

• hr_CI_type: The type of confidence interval for hazard ratio, either "Cox model" or "bootstrap".

• event_summary: A data frame containing the count and percentage of deaths and switches by treatment arm.

• data_switch: A list of input data for the switching models by treatment group. The variables include id, stratum, "tstart", "tstop", "cross", denominator, swtrt, and swtrt_time. For logistic switching models, stratum variables are converted to dummy variables, and natural cubic spline basis variables are created for the visit-specific intercepts.

• fit_switch: A list of fitted switching models for the denominator and numerator by treatment group.

• data_outcome: The input data for the outcome Cox model including the inverse probability of censoring weights. The variables include id, stratum, "tstart", "tstop", "event", "treated", "unstablized_weight", "stabilized_weight", base_cov, and treat.

• weight_summary: A data frame summarizing the weights by treatment arm.

• km_outcome: The Kaplan-Meier estimates of the survival functions for the treatment and control groups based on the weighted outcome data.

• lr_outcome: The log-rank test results for the treatment effect based on the weighted outcome data.

• fit_outcome: The fitted outcome Cox model.

• fail: Whether a model fails to converge.

• settings: A list containing the input parameter values.

• fail_boots: The indicators for failed bootstrap samples if boot is TRUE.

• fail_boots_data: The data for failed bootstrap samples if boot is TRUE.

• hr_boots: The bootstrap hazard ratio estimates if boot is TRUE.

Arguments

data

The input data frame that contains the following variables:

• id: The id to identify observations belonging to the same subject for counting process data with time-dependent covariates.

• stratum: The stratum.

• tstart: The starting time of the time interval for counting-process data with time-dependent covariates.

• tstop: The stopping time of the time interval for counting-process data with time-dependent covariates.

• event: The event indicator, 1=event, 0=no event.

• treat: The randomized treatment indicator, 1=treatment, 0=control.

• swtrt: The treatment switch indicator, 1=switch, 0=no switch.

• swtrt_time: The time from randomization to treatment switch.

• base_cov: The baseline covariates (excluding treat) used in the outcome model.

• numerator: The baseline covariates (excluding treat) used in the numerator switching model for stabilized weights.

• denominator: The baseline (excluding treat) and time-dependent covariates used in the denominator switching model.

id

The name of the id variable in the input data.

stratum

The name(s) of the stratum variable(s) in the input data.

tstart

The name of the tstart variable in the input data.

tstop

The name of the tstop variable in the input data.

event

The name of the event variable in the input data.

treat

The name of the treatment variable in the input data.

swtrt

The name of the swtrt variable in the input data.

swtrt_time

The name of the swtrt_time variable in the input data.

base_cov

The names of baseline covariates (excluding treat) in the input data for the Cox model.

numerator

The names of baseline covariates (excluding treat) in the input data for the numerator switching model for stabilized weights.

denominator

The names of baseline (excluding treat) and time-dependent covariates in the input data for the denominator switching model.

logistic_switching_model

Whether a pooled logistic regression switching model is used.

strata_main_effect_only

Whether to only include the strata main effects in the logistic regression switching model. Defaults to TRUE, otherwise all possible strata combinations will be considered in the switching model.

ns_df

Degrees of freedom for the natural cubic spline for visit-specific intercepts of the pooled logistic regression model. Defaults to 3 for two internal knots at the 33 and 67 percentiles of the treatment switching times.

firth

Whether the Firth's bias reducing penalized likelihood should be used.

flic

Whether to apply intercept correction to obtain more accurate predicted probabilities.

stabilized_weights

Whether to use the stabilized weights. The default is TRUE.

trunc

The truncation fraction of the weight distribution. Defaults to 0 for no truncation in weights.

trunc_upper_only

Whether to truncate the weights from the upper end of the weight distribution only. Defaults to TRUE, otherwise the weights will be truncated from both the lower and upper ends of the distribution.

swtrt_control_only

Whether treatment switching occurred only in the control group. The default is TRUE.

alpha

The significance level to calculate confidence intervals.

ties

The method for handling ties in the Cox model, either "breslow" or "efron" (default).

boot

Whether to use bootstrap to obtain the confidence interval for hazard ratio. Defaults to FALSE.

n_boot

The number of bootstrap samples.

seed

The seed to reproduce the bootstrap results. The default is NA, in which case, the seed from the environment will be used.

Author

Kaifeng Lu,

Details

The hazard ratio and confidence interval under a no-switching scenario are obtained as follows:

• Exclude all observations after treatment switch.

• Define the crossover and event indicators for the last time interval of each subject.

• For time-dependent Cox switching models, replicate unique event times across treatment arms within each subject.

• Fit the denominator switching model (and numerator model for stabilized weights) to estimate inverse probability of censoring weights. Either a Cox model with time-dependent covariates or a pooled logistic regression model can be used.

  • For the pooled logistic regression model, the probability of remaining uncensored (i.e., not switching) is calculated as \(1 - \hat{p}_{\text{switch}}\) and accumulated over time up to the start of each interval.

  • For the time-dependent Cox model, the probability of remaining unswitched is derived from the estimated baseline hazard and predicted risk score up to the end of each interval.

• Fit a weighted Cox model to the outcome survival times (excluding data after switching) to estimate the hazard ratio.

• Construct the p-value and confidence interval for the hazard ratio using either robust sandwich variance or bootstrapping. When bootstrapping is used, the confidence interval and p-value are based on a t-distribution with n_boot - 1 degrees of freedom.

References

James M. Robins and Dianne M. Finkelstein. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics. 2000;56(3):779-788.

Examples

# Example 1: pooled logistic regression switching model
library(dplyr)

sim1 <- 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)
  
fit1 <- ipcw(
  sim1[[1]], id = "id", tstart = "tstart", 
  tstop = "tstop", event = "event", treat = "trtrand", 
  swtrt = "xo", swtrt_time = "xotime", 
  base_cov = "bprog", numerator = "bprog", 
  denominator = c("bprog", "L"),
  logistic_switching_model = TRUE, ns_df = 3,
  swtrt_control_only = TRUE, boot = FALSE)
  
fit1 

# Example 2: time-dependent covariates Cox switching model

fit2 <- ipcw(
  shilong, id = "id", tstart = "tstart", tstop = "tstop", 
  event = "event", treat = "bras.f", swtrt = "co", 
  swtrt_time = "dco", 
  base_cov = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c", 
               "pathway.f"),
  numerator = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c", 
                "pathway.f"),
  denominator = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c",
                  "pathway.f", "ps", "ttc", "tran"),
  swtrt_control_only = FALSE, boot = FALSE)

fit2