tip_hr | R Documentation |
choose one of the following, and the other will be estimated:
exposure_confounder_effect
confounder_outcome_effect
tip_hr(
effect_observed,
exposure_confounder_effect = NULL,
confounder_outcome_effect = NULL,
verbose = getOption("tipr.verbose", TRUE),
hr_correction = FALSE
)
tip_hr_with_continuous(
effect_observed,
exposure_confounder_effect = NULL,
confounder_outcome_effect = NULL,
verbose = getOption("tipr.verbose", TRUE),
hr_correction = FALSE
)
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: |
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 |
Data frame.
## 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.