Inverse Probability of Censoring Weights (IPCW) Method for Treatment Switching
Uses the inverse probability of censoring weights (IPCW) method to obtain the hazard ratio estimate of the Cox model to adjust for treatment switching.
ipcw( data, id = "id", stratum = "", tstart = "tstart", tstop = "tstop", event = "event", treat = "treat", swtrt = "swtrt", swtrt_time = "swtrt_time", swtrt_time_lower = "", swtrt_time_upper = "", base_cov = "", numerator = "", denominator = "", logistic_switching_model = FALSE, strata_main_effect_only = TRUE, firth = FALSE, flic = FALSE, ns_df = 3, relative_time = TRUE, stabilized_weights = TRUE, trunc = 0, trunc_upper_only = TRUE, swtrt_control_only = TRUE, alpha = 0.05, ties = "efron", boot = TRUE, n_boot = 1000, seed = NA )
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.swtrt_time_lower: The lower bound of treatment switching time.swtrt_time_upper: The upper bound of treatment switching time.base_cov: The baseline covariates (excluding treat) used in the outcome model.numerator: The baseline covariates (excluding treat) used in the switching model for the numerator for stabilized weights.denominator: The baseline and time-dependent covariates (excluding treat) used in the switching model for the denominator.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.
swtrt_time_lower: The name of the swtrt_time_lower variable in the input data.
swtrt_time_upper: The name of the swtrt_time_upper 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 and time-dependent covariates (excluding treat) 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.
firth: Whether the Firth's bias reducing penalized likelihood should be used. The default is FALSE.
flic: Whether to apply intercept correction to obtain more accurate predicted probabilities. The default is FALSE.
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 artificial censoring times due to treatment switching.
relative_time: Whether to use the time relative to swtrt_time_lower as the intercepts for the pooled logistic regression model. The default is TRUE.
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. The default value is 0.05.
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 TRUE.
n_boot: The number of bootstrap samples.
seed: The seed to reproduce the bootstrap results. The default is missing, in which case, the seed from the environment will be used.
A list with the following components:
logrank_pvalue: The two-sided p-value of the log-rank test for an intention-to-treat (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 "Cox model" or "bootstrap".
data_switch: A list of input data for the switching models by treatment group.
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.
fit_outcome: The fitted outcome Cox model.
settings: A list with the following components:
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.firth: Whether the Firth's bias reducing penalized likelihood should be used.flic: Whether to apply intercept correction to obtain more accurate predicted probabilities.ns_df: Degrees of freedom for the natural cubic spline.relative_time: Whether to use the relative time as the intercepts.stabilized_weights: Whether to use the stabilized weights.trunc: The truncation fraction of the weight distribution.trunc_upper_only: Whether to truncate the weights from the upper end of the distribution only.swtrt_control_only Whether treatment switching occurred only in the control group.alpa: The significance level to calculate confidence intervals.ties: The method for handling ties in the Cox model.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.
We use the following steps to obtain the hazard ratio estimate and confidence interval had there been no treatment switching:
n_boot - 1 degrees of freedom.# Example 1: pooled logistic regression switching model sim1 <- tsegestsim( n = 500, allocation1 = 2, allocation2 = 1, pbprog = 0.5, trtlghr = -0.5, bprogsl = 0.3, shape1 = 1.8, scale1 = 0.000025, shape2 = 1.7, scale2 = 0.000015, 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, swtrt_control_only = TRUE, outputRawDataset = 1, seed = 2000) fit1 <- ipcw( sim1$paneldata, id = "id", tstart = "tstart", tstop = "tstop", event = "died", treat = "trtrand", swtrt = "xo", swtrt_time = "xotime", swtrt_time_lower = "timePFSobs", swtrt_time_upper = "xotime_upper", base_cov = "bprog", numerator = "bprog", denominator = "bprog*catlag", logistic_switching_model = TRUE, ns_df = 3, relative_time = TRUE, swtrt_control_only = TRUE, boot = FALSE) c(fit1$hr, fit1$hr_CI) # Example 2: time-dependent covariates Cox switching model fit2 <- ipcw( shilong, id = "id", tstart = "tstart", tstop = "tstop", event = "event", treat = "bras.f", swtrt = "co", swtrt_time = "dco", base_cov = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c", "pathway.f"), numerator = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c", "pathway.f"), denominator = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c", "pathway.f", "ps", "ttc", "tran"), swtrt_control_only = FALSE, boot = FALSE) c(fit2$hr, fit2$hr_CI)
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;56:779-788.
Kaifeng Lu, kaifenglu@gmail.com
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