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UNADJ binary estimator: unadjusted logistic / linear-probability regression

Source: R/bin_estimators.R
fit_unadj_bin.Rd

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

Usage

fit_unadj_bin(y, X, covars = NULL, effect_scale = c("logor", "riskdiff"))

Arguments

y

Numeric 0/1 outcome vector (length n).

X

Numeric exposure vector (length n).

covars

Optional data frame of covariates (n rows).

effect_scale

Character: "logor" (logistic) or "riskdiff" (linear probability model). Default "logor".

Value

Named list: beta, se, pvalue.

Details

When effect_scale = "logor" (default), fits a logistic regression and returns the conditional log-odds ratio for X. When effect_scale = "riskdiff", fits a linear probability model (OLS on the 0/1 outcome), returning a risk difference — a collapsible, linear alternative to the non-collapsible odds ratio.

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, outcome_type = "binary", seed = 1)
fit_unadj_bin(dat$y_bin, dat$X)
#> $beta
#> [1] 0.4066461
#> 
#> $se
#> [1] 0.1527228
#> 
#> $pvalue
#> [1] 0.007752997
#> 
fit_unadj_bin(dat$y_bin, dat$X, effect_scale = "riskdiff")
#> $beta
#> [1] 0.09560984
#> 
#> $se
#> [1] 0.03461115
#> 
#> $pvalue
#> [1] 0.006278234
#>