psrwe (version 3.2)

psrwe_survrmst: PS-Integrated Restricted Mean Survival Time (RMST) Test For Comparing Time-to-event Outcomes

Description

RMST test evaluates a two-arm RCT for up to a given time point. Variance can be estimated by the Jackknife method. Apply to the case when there is only one external data source and two-arm RCT.

Usage

psrwe_survrmst(
  dta_psbor,
  pred_tp,
  v_time = "time",
  v_event = "event",
  stderr_method = c("naive", "jk", "sjk", "cjk", "sbs", "cbs", "none"),
  n_bootstrap = 200,
  ...
)

Value

A data frame with class name PSRWE_RST_TESTANA. It contains the test statistics of each stratum as well as the Jackknife estimation. The results can be further summarized by its S3 method summary. The results can also be analyzed by psrwe_outana for outcome analysis and inference.

Arguments

dta_psbor

A class PSRWE_BOR object generated by psrwe_borrow.

pred_tp

A numeric value corresponding to the time of interest (e.g., 365 days or 1 year)

v_time

Column name corresponding to event time

v_event

Column name corresponding to event status

stderr_method

Method for computing StdErr, see Details

n_bootstrap

Number of bootstrap samples (for bootstrap stderr)

...

Additional Parameters

Details

stderr_method includes naive as default which mostly follows the Greenwood formula, jk using the Jackknife method within each stratum, sjk using simple Jackknife method for combined estimates such as point estimates in single-arm or treatment effects in RCT, or cjk for complex Jackknife method including refitting PS model, matching, trimming, calculating borrowing parameters, and combining overall estimates. Note that sjk may take a while longer to finish and cjk will take even longer to finish. The sbs and cbs is for simple and complex Bootstrap methods.

Examples

data(ex_dta_rct)
dta_ps_rct <- psrwe_est(ex_dta_rct,
                        v_covs = paste("V", 1:7, sep = ""),
                        v_grp = "Group", cur_grp_level = "current",
                        v_arm = "Arm", ctl_arm_level = "control",
                        ps_method = "logistic", nstrata = 5,
                        stra_ctl_only = FALSE)
ps_bor_rct <- psrwe_borrow(dta_ps_rct, total_borrow = 30)
rst_rmst <- psrwe_survrmst(ps_bor_rct,
                           pred_tp = 365,
                           v_time = "Y_Surv",
                           v_event = "Status")
rst_rmst