EValue (version 4.1.3)

multi_evalue: Calculate a multiple-bias E-value

Description

Calculate an E-value for a specified set of biases.

Usage

multi_evalue(biases, est, ...)
multi_evalues.HR(
  biases,
  est,
  lo = NA,
  hi = NA,
  rare = NULL,
  true = 1,
  verbose = TRUE,
  ...
)
multi_evalues.OR(
  biases,
  est,
  lo = NA,
  hi = NA,
  rare = NULL,
  true = 1,
  verbose = TRUE,
  ...
)
multi_evalues.RR(biases, est, lo = NA, hi = NA, true = 1, verbose = TRUE, ...)

Arguments

biases

An object created by multi_bias() (or a single bias) to include in the calculation of the E-value. May include any or all of confounding(), selection(), and misclassification(), and any of the options described in the documentation for those functions.

est

The effect estimate that was observed but which is suspected to be biased. This may be of class "estimate" (constructed with RR(), OR(), or HR(), or more information can be provided using the other arguments.

...

Arguments passed to other methods.

lo

Optional. Lower bound of the confidence interval. If not an object of class "estimate", assumed to be on the same scale as est.

hi

Optional. Upper bound of the confidence interval. If not an object of class "estimate", assumed to be on the same scale as est.

rare

Logical indicating whether outcome is sufficiently rare for risk ratio approximation to hold.

true

A number to which to shift the observed estimate to. Defaults to

1. If not an object of class "estimate", assumed to be on the same scale as est.

verbose

Logical indicating whether or not to print information about which parameters the multi-bias E-value refers to. Defaults to TRUE.

Value

Returns a multiple bias E-value, of class "multi_evalue", describing the value that each of a number of parameters would have to have for the observed effect measure to be completely explained by bias.

Examples

# NOT RUN {
# Calculate an E-value for unmeasured confounding
multi_evalue(est = RR(4), biases = confounding())
# Equivalent to
evalues.RR(4)

# Calculate a multi-bias E-value for selection bias
# and misclassification
multi_evalue(est = RR(2.5),
         biases = multi_bias(selection("selected"),
                   misclassification("outcome")))

# Calculate a multi-bias E-value for all three
# available types of bias
biases <- multi_bias(confounding(),
                     selection("general", "S = U"),
                     misclassification("exposure",
                            rare_outcome = TRUE))
multi_evalue(est = RR(2.5), biases = biases)

# Calculate a multi-bias E-value for a non-rare OR
# using the square root approximation
multi_evalue(est = OR(2.5, rare = FALSE), biases = biases)

# Calculate a non-null multi-bias E-value
multi_evalue(est = RR(2.5), biases = biases, true = 2)

# }