tip_coef | R Documentation |
choose one of the following, and the other will be estimated:
exposure_confounder_effect
confounder_outcome_effect
tip_coef( effect_observed, exposure_confounder_effect = NULL, confounder_outcome_effect = NULL, verbose = TRUE ) tip_coef_with_continuous( effect_observed, exposure_confounder_effect = NULL, confounder_outcome_effect = NULL, verbose = TRUE )
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: |
Data frame.
## 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) }
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