adjust_coef_with_binary {tipr}

Adjust an observed coefficient from a regression model with a binary confounder

Description

Adjust an observed coefficient from a regression model with a binary confounder

Usage

adjust_coef_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  loglinear = FALSE,
  verbose = getOption("tipr.verbose", TRUE)
)

Arguments

effect_observed

Numeric. Observed exposure - outcome effect from a loglinear model. This can be the beta coefficient, the lower confidence bound of the beta coefficient, or the upper confidence bound of the beta coefficient.

exposed_confounder_prev

Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population

unexposed_confounder_prev

Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population

confounder_outcome_effect

Numeric. Estimated relationship between the unmeasured confounder and the outcome.

loglinear

Logical. Calculate the adjusted coefficient from a loglinear model instead of a linear model (the default). When loglinear = FALSE, adjust_coef_with_binary() is equivalent to adjust_coef() where exposure_confounder_effect is the difference in prevalences.

verbose

Logical. Indicates whether to print informative message. Default: TRUE

Value

Data frame.

Examples

adjust_coef_with_binary(1.1, 0.5, 0.3, 1.3)