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)
#> ℹ The observed effect (1.1) is updated to 0.84 by a confounder with the
#>   following specifications:
#> • estimated prevalence of the unmeasured confounder in the exposed population:
#>   0.5
#> • estimated prevalence of the unmeasured confounder in the unexposed
#>   population: 0.3
#> • estimated relationship between the unmeasured confounder and the outcome: 1.3
#> # A tibble: 1 × 4
#>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…²
#>             <dbl>           <dbl>                   <dbl>                  <dbl>
#> 1            0.84             1.1                     0.2                    1.3
#> # ℹ abbreviated names: ¹exposure_confounder_effect, ²confounder_outcome_effect