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UNADJ survival estimator: unadjusted Cox / RMST regression

Source: R/surv_estimators.R
fit_unadj_surv.Rd

Regresses the survival outcome on the exposure X (plus covariates) with no instrument or negative-control adjustment. Bias reference.

Usage

fit_unadj_surv(
  time,
  event,
  X,
  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).

covars

Optional data frame of covariates (n rows).

effect_scale

Character: "loghr" (Cox) or "rmst" (pseudo-observation OLS). Default "loghr".

tau

RMST restriction time horizon. Default NULL (90th percentile of follow-up). Used only when effect_scale = "rmst".

Value

Named list: beta, se, pvalue.

Details

When effect_scale = "loghr" (default), fits a Cox proportional-hazards model and returns the log-hazard ratio for X. When effect_scale = "rmst", regresses leave-one-out RMST pseudo-observations (Graw et al. 2009) on X via OLS, returning an effect on the restricted-mean-survival-time (time) scale — a collapsible, linear alternative to the non-collapsible hazard ratio.

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, outcome_type = "survival", seed = 1)
fit_unadj_surv(dat$surv_time, dat$surv_event, dat$X)
#> $beta
#> [1] 0.5547407
#> 
#> $se
#> [1] 1.741489
#> 
#> $pvalue
#> [1] 5.581735e-08
#> 
fit_unadj_surv(dat$surv_time, dat$surv_event, dat$X, effect_scale = "rmst")
#> $beta
#> [1] -1.64363
#> 
#> $se
#> [1] 0.3177295
#> 
#> $pvalue
#> [1] 5.622797e-07
#>