tipr (version 1.0.2)

tip_coef: Tip a linear model coefficient with a continuous confounder.

Description

choose one of the following, and the other will be estimated:

• exposure_confounder_effect

• confounder_outcome_effect

Usage

tip_coef(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)
tip_coef_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)

Value

Data frame.

Arguments

effect_observed

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

exposure_confounder_effect

Numeric. Estimated scaled mean difference between the unmeasured confounder in the exposed population and unexposed population

confounder_outcome_effect

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

verbose

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

Examples

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_coef(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_coef(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = -0.05)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  lm(wt ~ mpg, data = mtcars) %>%
   broom::tidy(conf.int = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip_coef(confounder_outcome_effect = 2.5)
}