ipe: Iterative Parameter Estimation (IPE) for Treatment Switching

In trtswitch: Treatment Switching

Iterative Parameter Estimation (IPE) for Treatment Switching

Description

Obtains the causal parameter estimate from the accelerated failure-time (AFT) model and the hazard ratio estimate from the Cox model to adjust for treatment switching.

Usage

ipe(
  data,
  stratum = "",
  time = "time",
  event = "event",
  treat = "treat",
  rx = "rx",
  censor_time = "censor_time",
  base_cov = "",
  aft_dist = "weibull",
  strata_main_effect_only = 1,
  treat_modifier = 1,
  recensor = TRUE,
  admin_recensor_only = TRUE,
  autoswitch = TRUE,
  alpha = 0.05,
  ties = "efron",
  tol = 1e-06,
  boot = FALSE,
  n_boot = 1000,
  seed = NA
)

Arguments

data	The input data frame that contains the following variables: stratum: The stratum. time: The survival time for right censored data. event: The event indicator, 1=event, 0=no event. treat: The randomized treatment indicator, 1=treatment, 0=control. rx: The proportion of time on active treatment. censor_time: The administrative censoring time. It should be provided for all subjects including those who had events. base_cov: The baseline covariates (excluding treat).
stratum	The name(s) of the stratum variable(s) in the input data.
time	The name of the time 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.
rx	The name of the rx variable in the input data.
censor_time	The name of the censor_time variable in the input data.
base_cov	The names of baseline covariates (excluding treat) in the input data for the outcome Cox model.
aft_dist	The assumed distribution for time to event for the AFT model. Options include "exponential", "weibull", "loglogistic", and "lognormal".
strata_main_effect_only	Whether to only include the strata main effects in the AFT model. Defaults to TRUE, otherwise all possible strata combinations will be considered in the AFT model.
treat_modifier	The optional sensitivity parameter for the constant treatment effect assumption.
recensor	Whether to apply recensoring to counterfactual survival times. Defaults to TRUE.
admin_recensor_only	Whether to apply recensoring to administrative censoring times only. Defaults to TRUE. If FALSE, recensoring will be applied to the actual censoring times for dropouts.
autoswitch	Whether to exclude recensoring for treatment arms with no switching. Defaults to TRUE.
alpha	The significance level to calculate confidence intervals.
ties	The method for handling ties in the Cox model, either "breslow" or "efron" (default).
tol	The desired accuracy (convergence tolerance) for psi.
boot	Whether to use bootstrap to obtain the confidence interval for hazard ratio. Defaults to FALSE, in which case, the confidence interval will be constructed to match the log-rank test p-value.
n_boot	The number of bootstrap samples.
seed	The seed to reproduce the bootstrap results. The seed from the environment will be used if left unspecified.

Details

We use the following steps to obtain the hazard ratio estimate and confidence interval had there been no treatment switching:

• Use IPE to estimate the causal parameter \psi based on the AFT model for the observed survival times for the experimental arm and the counterfactual survival times for the control arm,

  U_{i,\psi} = T_{C_i} + e^{\psi}T_{E_i}

• Fit the Cox proportional hazards model to the observed survival times for the experimental group and the counterfactual survival times for the control group to obtain the hazard ratio estimate.

• Use either the log-rank test p-value for the intention-to-treat (ITT) analysis or bootstrap to construct the confidence interval for hazard ratio. If bootstrapping is used, the confidence interval and corresponding p-value are calculated based on a t-distribution with n_boot - 1 degrees of freedom.

Value

A list with the following components:

• psi: The estimated causal parameter.

• psi_CI: The confidence interval for psi.

• psi_CI_type: The type of confidence interval for psi, i.e., "log-rank p-value" or "bootstrap".

• 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 Cox model.

• 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 "log-rank p-value" or "bootstrap".

• Sstar: A data frame containing the counterfactual untreated survival times and event indicators for each treatment group.

• kmstar: A data frame containing the Kaplan-Meier estimates based on the counterfactual untreated survival times by treatment arm.

• data_aft: The input data for the AFT model for estimating psi.

• fit_aft: The fitted AFT model for estimating psi.

• data_outcome: The input data for the outcome Cox model.

• fit_outcome: The fitted outcome Cox model.

• settings: A list with the following components:

  • aft_dist: The distribution for time to event for the AFT model.

  • strata_main_effect_only: Whether to only include the strata main effects in the AFT model.

  • treat_modifier: The sensitivity parameter for the constant treatment effect assumption.

  • recensor: Whether to apply recensoring to counterfactual survival times.

  • admin_recensor_only: Whether to apply recensoring to administrative censoring times only.

  • autoswitch: Whether to exclude recensoring for treatment arms with no switching.

  • alpha: The significance level to calculate confidence intervals.

  • ties: The method for handling ties in the Cox model.

  • tol: The desired accuracy (convergence tolerance) for psi.

  • boot: Whether to use bootstrap to obtain the confidence interval for hazard ratio.

  • n_boot: The number of bootstrap samples.

  • seed: The seed to reproduce the bootstrap results.

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

• psi_boots: The bootstrap psi estimates if boot is TRUE.

Author(s)

Kaifeng Lu, [e-mail]

References

Michael Branson and John Whitehead. Estimating a treatment effect in survival studies in which patients switch treatment. Statistics in Medicine. 2002;21:2449-2463.

Ian R White. Letter to the Editor: Estimating treatment effects in randomized trials with treatment switching. Statistics in Medicine. 2006;25:1619-1622.

Examples

library(dplyr)

# Example 1: one-way treatment switching (control to active)

data <- immdef %>% mutate(rx = 1-xoyrs/progyrs)

fit1 <- ipe(
  data, time = "progyrs", event = "prog", treat = "imm", 
  rx = "rx", censor_time = "censyrs", aft_dist = "weibull",
  boot = FALSE)

c(fit1$hr, fit1$hr_CI)

# Example 2: two-way treatment switching (illustration only)

# the eventual survival time
shilong1 <- shilong %>%
  arrange(bras.f, id, tstop) %>%
  group_by(bras.f, id) %>%
  slice(n()) %>%
  select(-c("ps", "ttc", "tran"))

shilong2 <- shilong1 %>%
  mutate(rx = ifelse(co, ifelse(bras.f == "MTA", dco/ady, 
                                1 - dco/ady),
                     ifelse(bras.f == "MTA", 1, 0)))

fit2 <- ipe(
  shilong2, time = "tstop", event = "event",
  treat = "bras.f", rx = "rx", censor_time = "dcut",
  base_cov = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c",
               "pathway.f"),
  aft_dist = "weibull", boot = FALSE)

c(fit2$hr, fit2$hr_CI)

trtswitch documentation built on Nov. 2, 2024, 1:07 a.m.