tip_or | R Documentation |
choose one of the following, and the other will be estimated:
exposure_confounder_effect
confounder_outcome_effect
tip_or( effect_observed, exposure_confounder_effect = NULL, confounder_outcome_effect = NULL, verbose = TRUE, or_correction = FALSE ) tip_or_with_continuous( effect_observed, exposure_confounder_effect = NULL, confounder_outcome_effect = NULL, verbose = TRUE, or_correction = FALSE )
effect_observed |
Numeric positive value. Observed exposure - outcome odds ratio. This can be the point estimate, lower confidence bound, or upper confidence bound. |
exposure_confounder_effect |
Numeric. Estimated difference in scaled means 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: |
or_correction |
Logical. Indicates whether to use a correction factor.
The methods used for this function are based on risk ratios. For rare
outcomes, an odds ratio approximates a risk ratio. For common outcomes,
a correction factor is needed. If you have a common outcome (>15%),
set this to |
Data frame.
## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_or(1.2, exposure_confounder_effect = -2) ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_or(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) ## Example with broom if (requireNamespace("broom", quietly = TRUE) && requireNamespace("dplyr", quietly = TRUE)) { glm(am ~ mpg, data = mtcars, family = "binomial") %>% broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>% dplyr::filter(term == "mpg") %>% dplyr::pull(conf.low) %>% tip_or(confounder_outcome_effect = 2.5, or_correction = TRUE) }
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