tip_hr: Tip an observed hazard ratio with a normally distributed...

In tipr: Tipping Point Analyses

Tip an observed hazard ratio with a normally distributed confounder.

Description

choose one of the following, and the other will be estimated:

• exposure_confounder_effect

• confounder_outcome_effect

Usage

tip_hr(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = TRUE,
  hr_correction = FALSE
)

tip_hr_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  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 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

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_hr(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_hr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

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