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PGC2 / PGC2Gm survival mediation estimator: path-specific bridges with Cox / RMST

Source: R/surv_mediation.R
fit_pgc_mediation2_surv.Rd

Two-stage proximal mediation with path-specific negative controls and a survival outcome stage. Stages 1-2 (X bridge on W1, M bridge on W2, X_hat / M_hat construction) are identical to fit_pgc_mediation2 and remain OLS. Only stage 3 switches to Cox (log-HR) or RMST pseudo-observation OLS: Surv(t,e) ~ X_hat + M_hat + W_hat_X + W_hat_M + covars.

Usage

fit_pgc_mediation2_surv(
  time,
  event,
  X,
  M,
  g,
  W1,
  W2,
  gm = NULL,
  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).

W1

Numeric NC matrix (n x q) or vector for the X->M path.

W2

Numeric NC matrix (n x q) or vector for the M->Y path.

gm

Optional numeric mediator instrument (length n).

covars

Optional data frame of covariates (n rows).

min_f

Minimum partial F for G1. 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 G1 is below min_f.

Details

When gm = NULL (PGC2), stage 2 uses pure NC identification. When gm is supplied (PGC2Gm), the mediator instrument helps isolate conf_MY before bridging W2.

Examples

set.seed(1)
dat <- generate_toy_data(n = 500, outcome_type = "survival",
mo_confounding = 0.8, rho_G2 = 0.3,
lambda_XM = c(1, 0), lambda_MY = c(0, 1), seed = 1)
fit_pgc_mediation2_surv(dat$surv_time, dat$surv_event, dat$X, dat$M,
dat$G[, 1], dat$W1, dat$W2, gm = dat$Gm)
#> $NDE
#> [1] -0.6529615
#> 
#> $NDE_se
#> [1] 0.520502
#> 
#> $NDE_p
#> [1] 2.288635e-05
#> 
#> $NIE
#> [1] 0.8102306
#> 
#> $NIE_se
#> [1] 2.526972
#> 
#> $NIE_p
#> [1] 0.7484885
#> 
#> $alpha_M
#> [1] 0.5002583
#> 
#> $alpha_se
#> [1] 0.0116492
#> 
#> $beta_M
#> [1] 1.619625
#> 
#> $beta_M_se
#> [1] 5.051194
#>