IV2SLS survival mediation estimator: single-instrument 2SPS with Cox / RMST

Two-stage predictor substitution with a single instrument (G for X, no mediator instrument):

OLS: X ~ g + W + covars -> X_hat (purge U1 from X).
OLS: M ~ X_hat + covars -> alpha_M.
Cox / RMST: Surv(t,e) ~ X_hat + M + W + covars -> NDE (coef on X_hat), beta_M (coef on M).

NIE = alpha_M * beta_M. Weak-instrument gate (partial F for G) applies to the OLS first stage. Unlike fit_iv2sls_mediation2_surv, this estimator does not instrument the mediator and is NOT point-identified under M-O confounding — it is included for parity with the continuous fit_iv2sls_mediation.

Usage

fit_iv2sls_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 vector (length n) or matrix (n x q).
covars: Optional data frame of covariates (n rows).
min_f: Minimum partial F for the excluded instrument. Default 10.
effect_scale: Character: "loghr" or "rmst".
tau: RMST horizon (rmst only).

Value

Named list (same fields as fit_unadj_mediation_surv).

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, outcome_type = "survival", seed = 1)
fit_iv2sls_mediation_surv(dat$surv_time, dat$surv_event, dat$X, dat$M,
dat$G[, 1], dat$W[, 1])
#> $NDE
#> [1] -0.03876021
#> 
#> $NDE_se
#> [1] 0.9619814
#> 
#> $NDE_p
#> [1] 0.8662954
#> 
#> $NIE
#> [1] 0.4245667
#> 
#> $NIE_se
#> [1] 1.169592
#> 
#> $NIE_p
#> [1] 0.7166018
#> 
#> $alpha_M
#> [1] 0.5015545
#> 
#> $alpha_se
#> [1] 0.02736802
#> 
#> $beta_M
#> [1] 0.8465018
#> 
#> $beta_M_se
#> [1] 2.331477
#>