adjust_coef_with_binary: Adjust an observed coefficient from a regression model with a...

In tipr: Tipping Point Analyses

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)

tipr documentation built on May 29, 2024, 9:59 a.m.