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Tip a regression coefficient using the partial R2 for an unmeasured confounder-exposure relationship and unmeasured confounder- outcome relationship

Source: R/tip_coef_with_r2.R
tip_coef_with_r2.Rd

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

  • confounder_exposure_r2

  • confounder_outcome_r2

Usage

tip_coef_with_r2(
  effect_observed,
  se,
  df,
  confounder_exposure_r2 = NULL,
  confounder_outcome_r2 = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  alpha = 0.05,
  tip_bound = FALSE,
  ...
)

Arguments

effect_observed

Numeric. Observed exposure - outcome effect from a regression model. This is the point estimate (beta coefficient)

se

Numeric. Standard error of the effect_observed in the previous parameter.

df

Numeric positive value. Residual degrees of freedom for the model used to estimate the observed exposure - outcome effect. This is the total number of observations minus the number of parameters estimated in your model. Often for models estimated with an intercept this is N - k - 1 where k is the number of predictors in the model.

confounder_exposure_r2

Numeric value between 0 and 1. The assumed partial R2 of the unobserved confounder with the exposure given the measured covariates.

confounder_outcome_r2

Numeric value between 0 and 1. The assumed partial R2 of the unobserved confounder with the outcome given the exposure and the measured covariates.

verbose

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

alpha

Significance level. Default = 0.05.

tip_bound

Do you want to tip at the bound? Default = FALSE, will tip at the point estimate

...

Optional arguments passed to the sensemakr::adjusted_estimate() function.

Value

A data frame.

Examples

tip_coef_with_r2(0.5, 0.1, 102, 0.5)
#> # A tibble: 1 × 10
#>   effect_adjusted lb_adjusted ub_adjusted effect_observed lb_observed
#>             <dbl>       <dbl>       <dbl>           <dbl>       <dbl>
#> 1        5.55e-17      -0.245       0.245             0.5       0.302
#> # ℹ 5 more variables: ub_observed <dbl>, se_observed <dbl>, df_observed <dbl>,
#> #   confounder_exposure_r2 <dbl>, confounder_outcome_r2 <dbl>