# multi_evalue: Calculate a multiple-bias E-value In EValue: Sensitivity Analyses for Unmeasured Confounding and Other Biases in Observational Studies and Meta-Analyses

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```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 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```# 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) ```

EValue documentation built on Oct. 28, 2021, 9:10 a.m.