Adjust an observed hazard ratio for a normally distributed confounder

Adjust an observed hazard ratio for a normally distributed confounder

Usage

adjust_hr(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

adjust_hr_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

Arguments

effect_observed

Numeric positive value. Observed exposure - outcome 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. Estimated relationship between the unmeasured confounder and the outcome.

verbose

Logical. Indicates whether to print informative message. Default: TRUE

hr_correction

Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, a hazard 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

adjust_hr(0.9, -0.9, 1.3)
#> ℹ The observed effect (HR: 0.9) is updated to HR: 1.14 by a confounder with the
#>   following specifications:
#> • estimated difference in scaled means: -0.9
#> • estimated relationship (HR) between the unmeasured confounder and the
#>   outcome: 1.3
#> # A tibble: 1 × 4
#>   hr_adjusted hr_observed exposure_confounder_effect confounder_outcome_effect
#>         <dbl>       <dbl>                      <dbl>                     <dbl>
#> 1        1.14         0.9                       -0.9                       1.3