Skip to contents

Tip an observed odds ratio with a binary confounder.

Source: R/tip_with_binary.R
tip_or_with_binary.Rd

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_or_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

Arguments

effect_observed

Numeric positive value. Observed exposure - outcome odds 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

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 TRUE. Default: FALSE.

Value

Data frame.

Examples

tip_or_with_binary(0.9, 0.9, 0.1)
#> ℹ The observed effect (0.9) WOULD be tipped by 1 unmeasured confounder with the
#>   following specifications:
#> • estimated prevalence of the unmeasured confounder in the exposed population:
#>   0.9
#> • estimated prevalence of the unmeasured confounder in the unexposed
#>   population: 0.1
#> • estimated relationship between the unmeasured confounder and the outcome:
#>   0.88
#> # A tibble: 1 × 6
#>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹
#>             <dbl>           <dbl>                   <dbl>                  <dbl>
#> 1               1             0.9                     0.9                    0.1
#> # ℹ abbreviated name: ¹​unexposed_confounder_prev
#> # ℹ 2 more variables: confounder_outcome_effect <dbl>,
#> #   n_unmeasured_confounders <dbl>