PGC survival estimator: proxy G-component correction with Cox / RMST

Three-step bridge-function estimator with a survival outcome stage:

1. Residualise X on G -> X_resid (OLS).

2. Bridge X_resid on the FULL W matrix -> W_hat (OLS).

3. Regress the survival outcome on X + W_hat via Cox (log-HR) or RMST pseudo-observation OLS.

Usage

fit_pgc_surv(
  time,
  event,
  X,
  g,
  W,
  covars = NULL,
  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).

g

Numeric instrument vector (length n).

W

Numeric NC matrix (n x q) or vector (length n).

covars

Optional data frame of covariates (n rows).

effect_scale

Character: "loghr" or "rmst".

tau

RMST horizon (rmst only).

Value

Named list: beta, se, pvalue.

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, outcome_type = "survival", seed = 1)
fit_pgc_surv(dat$surv_time, dat$surv_event, dat$X, dat$G[, 1], dat$W)
#> $beta
#> [1] 0.3859335
#> 
#> $se
#> [1] 1.470987
#> 
#> $pvalue
#> [1] 0.002221541
#>