View source: R/tip_with_binary.R
tip_with_binary | R Documentation |
Choose two of the following three to specify, and the third will be estimated:
exposed_confounder_prev
unexposed_confounder_prev
confounder_outcome_effect
Alternatively, specify all three and the function will return the number of unmeasured confounders specified needed to tip the analysis.
tip_with_binary( effect_observed, exposed_confounder_prev = NULL, unexposed_confounder_prev = NULL, confounder_outcome_effect = NULL, verbose = TRUE, correction_factor = "none" ) tip_b( effect_observed, exposed_confounder_prev = NULL, unexposed_confounder_prev = NULL, confounder_outcome_effect = NULL, verbose = TRUE, correction_factor = "none" )
effect_observed |
Numeric positive value. Observed exposure - outcome effect (assumed to be the exponentiated coefficient, so a risk ratio, odds ratio, or hazard ratio). This can be the point estimate, lower confidence bound, or upper confidence bound. |
exposed_confounder_prev |
Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population |
unexposed_confounder_prev |
Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the 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: |
correction_factor |
Character string. Options are "none", "hr", "or". For common outcomes (>15%), the odds ratio or hazard ratio is not a good estimate for the risk ratio. In these cases, we can apply a correction factor. If you are supplying a hazard ratio for a common outcome, set this to "hr"; if you are supplying an odds ratio for a common outcome, set this to "or"; if you are supplying a risk ratio or your outcome is rare, set this to "none" (default). |
tip_b()
is an alias for tip_with_binary()
.
## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_with_binary(1.2, exposed_confounder_prev = 0.5, unexposed_confounder_prev = 0) ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_with_binary(1.2, exposed_confounder_prev = 0.5, unexposed_confounder_prev = 0, confounder_outcome_effect = 1.1) ## 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_with_binary(exposed_confounder_prev = 1, confounder_outcome_effect = 1.15) }
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