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.

## Usage

```
tip_with_binary(
effect_observed,
exposed_confounder_prev = NULL,
unexposed_confounder_prev = NULL,
confounder_outcome_effect = NULL,
verbose = getOption("tipr.verbose", TRUE),
correction_factor = "none"
)
tip_b(
effect_observed,
exposed_confounder_prev = NULL,
unexposed_confounder_prev = 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.

- 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:

`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_with_binary(1.2, exposed_confounder_prev = 0.5, unexposed_confounder_prev = 0)
#> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the
#> following specifications:
#> • estimated prevalence of the unmeasured confounder in the exposed population:
#> 0.5
#> • estimated prevalence of the unmeasured confounder in the unexposed
#> population: 0
#> • estimated relationship between the unmeasured confounder and the outcome: 1.4
#> # A tibble: 1 × 6
#> effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1.2 0.5 0
#> # ℹ abbreviated name: ¹unexposed_confounder_prev
#> # ℹ 2 more variables: confounder_outcome_effect <dbl>,
#> # n_unmeasured_confounders <dbl>
## 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)
#> ℹ The observed effect (1.2) WOULD be tipped by 4 unmeasured confounders with
#> the following specifications:
#> • estimated prevalence of the unmeasured confounder in the exposed population:
#> 0.5
#> • estimated prevalence of the unmeasured confounder in the unexposed
#> population: 0
#> • estimated relationship between the unmeasured confounder and the outcome: 1.1
#> # A tibble: 1 × 6
#> effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1.2 0.5 0
#> # ℹ abbreviated name: ¹unexposed_confounder_prev
#> # ℹ 2 more variables: confounder_outcome_effect <dbl>,
#> # 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_with_binary(exposed_confounder_prev = 1, confounder_outcome_effect = 1.15)
}
#> ℹ The observed effect (1.13) WOULD be tipped by 1 unmeasured confounder with
#> the following specifications:
#> • estimated prevalence of the unmeasured confounder in the exposed population:
#> 1
#> • estimated prevalence of the unmeasured confounder in the unexposed
#> population: 0.12
#> • estimated relationship between the unmeasured confounder and the outcome:
#> 1.15
#> # A tibble: 1 × 6
#> effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1.13 1 0.119
#> # ℹ abbreviated name: ¹unexposed_confounder_prev
#> # ℹ 2 more variables: confounder_outcome_effect <dbl>,
#> # n_unmeasured_confounders <dbl>
```