![](../logo.png)
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.
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>