adjust_hr: Adjust an observed hazard ratio for a normally distributed...

In tipr: Tipping Point Analyses

Adjust an observed hazard ratio for a normally distributed confounder

Description

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)

tipr documentation built on May 29, 2024, 9:59 a.m.