DIRECT binary estimator: logistic / LPM with instrument and NC covariates

Regresses the binary 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_bin(
  y,
  X,
  g,
  w,
  covars = NULL,
  effect_scale = c("logor", "riskdiff")
)

Arguments

y

Numeric 0/1 outcome vector (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: "logor" or "riskdiff".

Value

Named list: beta, se, pvalue.

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, outcome_type = "binary", seed = 1)
fit_direct_bin(dat$y_bin, dat$X, dat$G[, 1], dat$W[, 1])
#> $beta
#> [1] 0.5398956
#> 
#> $se
#> [1] 0.2533408
#> 
#> $pvalue
#> [1] 0.03308055
#>