R/PDRobust-package.R

In PDRobust: Robust Longitudinal Effects Under Truncation by Death

#' PDRobust: Principal-stratification treatment-effect estimation
#'
#' PDRobust estimates treatment effects for longitudinal outcomes that may be
#' unavailable after death. The effects apply to the always-survivor principal
#' stratum: subjects who would survive through the selected cutoff time under
#' either treatment. `Mapping()` identifies the variables and analysis times,
#' and `DataStandard()` prepares the data for the remaining functions.
#'
#' `PSPred()`, `PrinPred()`, and `OutPred()` fit their models each time they are
#' called and return numeric predictions. `HTESepT()` estimates effects
#' separately at user-selected times, whereas `HTEAllT()` estimates a joint
#' trajectory over every observed time from baseline through cutoff.
#'
#' @section Treatment coding:
#' The implemented estimator uses treatment `1` as the survival-favorable arm:
#' potential survival satisfies \eqn{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.
#'
#' @section Interpretation and assumptions:
#' The target population comprises subjects who would survive through the
#' selected cutoff under either treatment. The effect model describes outcome
#' differences at earlier analysis times within that fixed population.
#' Continuous-outcome models describe effects on a linear scale. Binary-outcome
#' models transform the model's linear predictor with
#' `2 * plogis(eta) - 1`; their coefficients are not odds ratios.
#'
#' Causal interpretation requires consistency, no interference, adequate
#' treatment and survival overlap, treatment ignorability conditional on the
#' measured covariates, the stated survival monotonicity, and principal
#' ignorability. Checking the data and covariate balance cannot establish these
#' assumptions. Triple robustness means that, under the method's assumptions
#' and regularity conditions, at least two of the propensity score, principal
#' score, and outcome mean models must be correctly specified. It does not
#' guarantee unbiased results in every finite sample. Limiting extreme
#' probabilities can also affect the estimates.
#'
#' `SA()` examines sensitivity by adding random noise to the outcome. It does
#' not vary the principal-ignorability assumption. See the `method-and-coding`
#' vignette for treatment coding and other implementation limits.
#'
#' @references
#' Zhang, Y., Shardell, M., Falvey, J., McCoy, R., Stuart, E., and Chen, C.
#' (2026). A Novel Tool for Evaluating Effect Modification in Older Adults
#' with ADRD Using Medicare Claims. arXiv:2608.06654.
#' \doi{10.48550/arXiv.2608.06654}.
#'
#' @importFrom utils globalVariables
#' @keywords internal
"_PACKAGE"

globalVariables(c(
  "estimate", "time", "covariate", "term", "adjustment", "smd",
  "statistic", "covname", "estcoef", "lowerbd", "upperbd", "ratio",
  "LowerBound", "UpperBound",".pd_weights"
))