Skip to contents

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

Source: R/adjust_coef_with_r2.R
adjust_coef_with_r2.Rd

This function wraps the sensemakr::adjusted_estimate() and sensemakr::adjusted_se() functions.

Usage

adjust_coef_with_r2(
  effect_observed,
  se,
  df,
  confounder_exposure_r2,
  confounder_outcome_r2,
  verbose = getOption("tipr.verbose", TRUE),
  alpha = 0.05,
  ...
)

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.

...

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

Value

A data frame.

References

Carlos Cinelli, Jeremy Ferwerda and Chad Hazlett (2021). sensemakr: Sensitivity Analysis Tools for Regression Models. R package version 0.1.4. https://CRAN.R-project.org/package=sensemakr

Examples

adjust_coef_with_r2(0.5, 0.1, 102, 0.05, 0.1)
#> # A tibble: 1 × 10
#>   effect_adjusted lb_adjusted ub_adjusted effect_observed lb_observed
#>             <dbl>       <dbl>       <dbl>           <dbl>       <dbl>
#> 1           0.427       0.233       0.621             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>