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IV2SLS survival estimator: two-stage predictor substitution with Cox / RMST

Source: R/surv_estimators.R
fit_iv2sls_surv.Rd

Two-stage predictor substitution (2SPS): the first stage regresses X on the instrument G (plus W and covariates) via OLS, producing fitted \(\hat X\); the second stage regresses the survival outcome on \(\hat X\) (plus W and covariates) via Cox (log-HR) or RMST pseudo-observation OLS.

Usage

fit_iv2sls_surv(
  time,
  event,
  X,
  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).

g

Numeric instrument vector (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: beta, se, pvalue.

Details

A weak-instrument check (partial F for the excluded instrument G, Stock & Yogo 2005) is applied to the OLS first stage. If the partial F is below min_f, the function returns NA.

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, outcome_type = "survival", seed = 1)
fit_iv2sls_surv(dat$surv_time, dat$surv_event, dat$X, dat$G[, 1], dat$W[, 1])
#> $beta
#> [1] 0.3740847
#> 
#> $se
#> [1] 1.45366
#> 
#> $pvalue
#> [1] 0.03551488
#>