adjust_hr_with_binary: Adjust an observed hazard ratio with a binary confounder

In tipr: Tipping Point Analyses

Adjust an observed hazard ratio with a binary confounder

Description

Adjust an observed hazard ratio with a binary confounder

Usage

adjust_hr_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  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.
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
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_with_binary(0.8, 0.1, 0.5, 1.8)

tipr documentation built on Sept. 5, 2022, 5:09 p.m.