choose one of the following, and the other will be estimated:

`exposure_confounder_effect`

`confounder_outcome_effect`

## Usage

```
tip(
effect_observed,
exposure_confounder_effect = NULL,
confounder_outcome_effect = NULL,
verbose = getOption("tipr.verbose", TRUE),
correction_factor = "none"
)
tip_with_continuous(
effect_observed,
exposure_confounder_effect = NULL,
confounder_outcome_effect = NULL,
verbose = getOption("tipr.verbose", TRUE),
correction_factor = "none"
)
tip_c(
effect_observed,
exposure_confounder_effect = NULL,
confounder_outcome_effect = NULL,
verbose = getOption("tipr.verbose", TRUE),
correction_factor = "none"
)
```

## Arguments

- 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.

- 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:

`TRUE`

- 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).

## Examples

```
## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip(1.2, exposure_confounder_effect = -2)
#> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the
#> following specifications:
#> • estimated difference in scaled means between the unmeasured confounder in the
#> exposed population and unexposed population: -2
#> • estimated relationship between the unmeasured confounder and the outcome:
#> 0.91
#> # A tibble: 1 × 5
#> effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…²
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1.2 -2 0.913
#> # ℹ abbreviated names: ¹exposure_confounder_effect, ²confounder_outcome_effect
#> # ℹ 1 more variable: n_unmeasured_confounders <dbl>
## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)
#> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with
#> the following specifications:
#> • estimated difference in scaled means between the unmeasured confounder in the
#> exposed population and unexposed population: -2
#> • estimated relationship between the unmeasured confounder and the outcome:
#> 0.99
#> # A tibble: 1 × 5
#> effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…²
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1.2 -2 0.99
#> # ℹ abbreviated names: ¹exposure_confounder_effect, ²confounder_outcome_effect
#> # ℹ 1 more variable: n_unmeasured_confounders <dbl>
## 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(confounder_outcome_effect = 2.5)
}
#> ℹ The observed effect (1.13) WOULD be tipped by 1 unmeasured confounder with
#> the following specifications:
#> • estimated difference in scaled means between the unmeasured confounder in the
#> exposed population and unexposed population: 0.13
#> • estimated relationship between the unmeasured confounder and the outcome: 2.5
#> # A tibble: 1 × 5
#> effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…²
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1.13 0.133 2.5
#> # ℹ abbreviated names: ¹exposure_confounder_effect, ²confounder_outcome_effect
#> # ℹ 1 more variable: n_unmeasured_confounders <dbl>
```