tipr (version 0.4.0)

adjust_coef_with_r2: Adjust a regression coefficient using the partial R2 for an unmeasured confounder-exposure relationship and unmeasured confounder- outcome relationship

Description

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

Usage

adjust_coef_with_r2(
  effect,
  se,
  df,
  exposure_r2,
  outcome_r2,
  verbose = TRUE,
  alpha = 0.05,
  ...
)

Arguments

effect

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

se

Numeric. Standard error of the effect 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.

exposure_r2

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

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

# NOT RUN {
adjust_coef_with_r2(0.5, 0.1, 102, 0.05, 0.1)
# }