HTESepT: Separate estimation of heterogeneous treatment effects by...

In PDRobust: Robust Longitudinal Effects Under Truncation by Death

Separate estimation of heterogeneous treatment effects by time point

Description

Performs separate analyses of heterogeneous treatment effects at each selected time point.

Usage

HTESepT(
  data,
  ps_fo,
  prin_fo,
  out_fo,
  target_time,
  B,
  conf_level = 0.95,
  max_attempts = NULL,
  verbose = TRUE,
  progress_callback = NULL
)

Arguments

data	Data prepared by DataStandard().
ps_fo	propensity score model formula
prin_fo	principal score model formula
out_fo	outcome mean model formula
target_time	A non-empty numeric vector containing timepoints of interest in standardized form. Baseline is allowed.
B	Number of bootstrap replications. Use 0 for point estimates only.
conf_level	The confidence level for Wald intervals calculated from bootstrap standard errors.
max_attempts	The maximum number of resampling attempts allowed to obtain B successful bootstrap replications. Defaults to ⁠10B⁠. Resampling stops once B successful replications are obtained or when the maximum number of attempts is reached, whichever occurs first. Thus, fewer than B successful replications may be returned if the maximum number of attempts is reached.
verbose	If TRUE, print bootstrap progress messages.
progress_callback	An optional function for receiving bootstrap progress updates. It is called before model fitting, after the point estimate, after every bootstrap attempt, and when bootstrapping finishes. Each update is a named list containing stage, successful, requested, attempts, max_attempts, failed_attempts, complete, elapsed_seconds, and updated_at. If the callback produces an error, the function warns once and stops sending updates; the statistical analysis continues.

Details

HTESepT() combines predictions from the propensity score, principal score, and outcome mean models to form the data used for effect estimation. It fits a separate treatment-effect model at each selected time and, when requested, uses bootstrap resampling to calculate confidence intervals.

Value

A pd_hte_timevarying object containing the estimate for each requested time, confidence intervals when B > 0, model-checking information, and a summary of successful and failed bootstrap attempts. Displayed estimates are rounded to three decimal places; boot_mat stores the unrounded bootstrap coefficients.

Numerical safeguards

Propensity scores are limited to ⁠[0.01, 0.99]⁠. Their product with estimated survival probabilities under treatment 1 is limited to ⁠[0.005, 0.995]⁠ when effects are estimated. These limits prevent division by probabilities very close to zero, but they can affect the estimates and do not demonstrate adequate overlap or validate the causal assumptions. Review the returned model information, especially when few subjects remain at risk.

Treatment coding

The implemented estimator uses treatment 1 as the survival-favorable arm: potential survival satisfies S^1 \ge S^0 at cutoff. Its always-survivor principal score is therefore the survival probability under treatment 0. If the survival-favorable arm is coded as 0 in the raw data, recode the raw treatment as 1 - A before mapping and standardizing. To report the original contrast, negate the package estimate and transform an interval ⁠[lower, upper]⁠ to ⁠[-upper, -lower]⁠. Mapping() does not infer or reverse treatment coding.

See Also

PDRobust-package, pd_methods

Examples

data("BiSample", package = "PDRobust")
map <- Mapping(
  id = "id", time = "time", treatment = "A",
  survival = "S", outcome = "Y",
  baseline_time = 0, cutoff_time = 2,
  covariates = c("X1", "X2", "X4"),
  interest_vars = c("X1", "X2"), y_type = "B"
)
pd_dat <- DataStandard(BiSample, map)
fit <- HTESepT(
  pd_dat,
  A ~ X1 + X2 + X4,
  S ~ X1 + X2 + X4 + A + time,
  Y ~ X1 + X2 + A,
  target_time = c(0, 2), B = 0
)
fit$summary

PDRobust documentation built on Oct. 2, 2026, 5:09 p.m.