Misclassification — misclassification • EValue

A type of bias. Declares that (differential) misclassification will be a component of interest in the multi-bias sensitivity analysis. Generally used within other functions; its output is returned invisibly.

misclassification(
  ...,
  rare_outcome = FALSE,
  rare_exposure = FALSE,
  verbose = FALSE
)

Arguments

...	Arguments describing the type of misclassification. Currently two options: "outcome" or "exposure".
rare_outcome	Logical. Is the outcome rare enough that outcome odds ratios approximate risk ratios? Only needed when considering exposure misclassification. Note that `rare_outcome = FALSE` returns an error, as this option is not currently available.
rare_exposure	Logical. Is the exposure rare enough that exposure odds ratios approximate risk ratios? Only needed when considering exposure misclassification.
verbose	Logical. If `TRUE`, returns warnings and messages immediately. Defaults to `FALSE` because it is generally used within the `multi_bias()` function, which will print the same messages/warnings.

Value

Invisibly returns a list with components whose values depend on the options chosen: n (the degree of the polynomial in the numerator), d (the degree of the polynomial in the denominator), m (the parameters in the bias factor), mess (any messages/warnings that should be printed for the user), and bias("misclassification").

Examples

# returns invisibly without print()
print(misclassification("outcome"))
#> [1] "outcome misclassification"

# Calculate an E-value for misclassification
multi_evalue(est = RR(4),
         biases = misclassification("exposure",
                  rare_outcome = TRUE, rare_exposure = TRUE))
#> This multi-bias e-value refers simultaneously to parameters RR_YA*|a .
#> (See documentation for details.)
#>                     point lower upper
#> RR                      4    NA    NA
#> Multi-bias E-values     4    NA    NA