tipr (version 0.4.0)

tip_lm: Tip a linear model result with a continuous confounder.

Description

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

• smd

• outcome_association

Usage

tip_lm(
  effect,
  smd = NULL,
  outcome_association = NULL,
  verbose = TRUE,
  correction_factor = "none"
)
lm_tip(effect, smd, outcome_association, verbose = TRUE)

Arguments

effect

Numeric. Observed exposure - outcome effect from a regression model. This can be the beta coefficient, the lower confidence bound of the beta coefficient, or the upper confidence bound of the beta coefficient.

smd

Numeric. Estimated scaled mean difference between the unmeasured confounder in the exposed population and unexposed population

outcome_association

Numeric positive value. Estimated association between the unmeasured confounder and the outcome

verbose

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

correction_factor

Character string. Options are "none", "hr", "or". For common outcomes (>15%), the odds ratio or hazard ratio is not a good estimate for the relative risk. In these cases, we can apply a correction factor. If you are supplying a hazard ratio for a common outcome, set this to "hr"; if you are supplying an odds ratio for a common outcome, set this to "or"; if you are supplying a risk ratio or your outcome is rare, set this to "none" (default).

Value

Data frame.

Examples

# NOT RUN {
## to estimate the association between an unmeasured confounder and outcome
## needed to tip analysis
tip_lm(1.2, smd = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_lm(1.2, smd = -2, outcome_association = -0.05)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  lm(wt ~ mpg, data = mtcars) %>%
   broom::tidy(conf.int = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip_lm(outcome_association = 2.5)
}
# }