PGC survival mediation estimator: single-panel bridge with Cox / RMST

Bridge-function-adjusted natural direct and indirect effects with a survival outcome stage, using a single negative-control panel W for both the X->M and M->Y confounding paths. This is the survival analogue of fit_pgc_mediation (continuous outcome):

OLS: residualise X on G -> X_resid.
OLS: bridge X_resid on the FULL W matrix -> W_hat (proxy for U).
OLS: M ~ X + W_hat + covars -> alpha_M.
Cox / RMST: Surv(t,e) ~ X + M + W_hat + covars -> NDE (coef on X), beta_M (coef on M).

NIE = alpha_M * beta_M. Unlike fit_pgc_mediation2_surv, which uses path-specific W1/W2 bridges, this estimator uses a single combined W panel and is appropriate when separate conf_XM / conf_MY confounders are not assumed.

Usage

fit_pgc_mediation_surv(
  time,
  event,
  X,
  M,
  g,
  W,
  covars = NULL,
  min_f = 10,
  effect_scale = c("loghr", "rmst"),
  tau = NULL
)

Arguments

time: Numeric follow-up time vector (length n).
event: Numeric 0/1 event indicator (length n).
X: Numeric exposure vector (length n).
M: Numeric mediator vector (length n).
g: Numeric instrument for X (length n).
W: Numeric NC matrix (n x q) or vector (length n).
covars: Optional data frame of covariates (n rows).
min_f: Minimum partial F for G. Default 10.
effect_scale: Character: "loghr" or "rmst".
tau: RMST horizon (rmst only).

Value

Named list (same fields as fit_unadj_mediation_surv). Returns all-NA if the first-stage partial F for G is below min_f.

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, outcome_type = "survival", seed = 1)
fit_pgc_mediation_surv(dat$surv_time, dat$surv_event, dat$X, dat$M,
dat$G[, 1], dat$W)
#> $NDE
#> [1] 0.2197512
#> 
#> $NDE_se
#> [1] 1.245767
#> 
#> $NDE_p
#> [1] 0.7995041
#> 
#> $NIE
#> [1] 0.1678713
#> 
#> $NIE_se
#> [1] 0.697834
#> 
#> $NIE_p
#> [1] 0.8098957
#> 
#> $alpha_M
#> [1] 0.4982158
#> 
#> $alpha_se
#> [1] 0.005077079
#> 
#> $beta_M
#> [1] 0.336945
#> 
#> $beta_M_se
#> [1] 1.400662
#>