DIRECT survival estimator: Cox / RMST with instrument and NC covariates

Regresses the survival outcome on X plus the genetic instrument G, the negative-control panel W, and covariates. Naive adjustment; does not correct for unmeasured confounding via a ratio or IV approach.

Usage

fit_direct_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 vector (length n) or matrix (n x q).

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_direct_surv(dat$surv_time, dat$surv_event, dat$X, dat$G[, 1], dat$W[, 1])
#> $beta
#> [1] 0.4221789
#> 
#> $se
#> [1] 1.525281
#> 
#> $pvalue
#> [1] 0.005884582
#>