interpret_equivalence {confinterpret}

Equivalence test interpretations of confidence intervals.

Description

Conduct equivalence tests on confidence intervals using a standard set of interpretations. Takes a confidence interval around an effect size measure, for example from the results from a randomised controlled trial comparing the outcome for an intervention group to a control group.

Usage

interpret_equivalence(ci, actual_null = 0, eq_margin = 0.1,
  groups = c("Control intervention", "Test intervention"),
  beneficial_outcome = TRUE)

Arguments

ci

A single row from a matrix of the type returned by confint(), containing the confidence interval for the parameter estimate. The two columns provide the lower and upper confidence limits.

actual_null

The value that precisely zero difference would have in the parameter being examined. For an absolute measure this will typically be 0. For a relative measure it will typically be 1. This is the starting point that the eq_margin is applied to in order to establish the region for comparison.

eq_margin

Numerical value specifying the equivalence margin to be used.

groups

A character vector of length 2 containing short descriptive names of the groups being compared, such as the names of the interventions being compared if the confidence interval is derived from an outcome effect size measure in a randomised controlled trial. Give the name of the intervention given to the comparison or control group first and the new or tested intervention second.

beneficial_outcome

Is the outcome to be treated as beneficial (i.e., a higher value of the outcome is superior)? For harmful outcomes (where lower numbers are better), set this to FALSE. If, for example, the outcome is measuring something like prevalence of patients recovering from a disease, that is likely to be beneficial; if it is measuring the prevalence of patients falling ill with a disease it is likely to be not beneficial.

Details

Equivalence tests can be specified in analysis plans when the aim is to check whether a new intervention performs the same as an old one. The test is most appropriate where the aim is not to result in a better or worse outcome, but the same as under the previous intervention. One particular use is for testing new versions of medicines, such as generic versions of drugs after the branded version's patent protection has ended. In this situation, if the generic manufacturer is correctly producing the medicine it should result in neither better nor worse outcomes than the branded medicine.

When conducting equivalence tests, an equivalence margin is specified. This is the region around a true null (i.e., no difference) result that is deemed to be within a reasonable range. It is commonly selected to include the range of differences that would be of no practical significance.

You are able to supply descriptive names of the interventions being compared, and these will be inserted into the resultant interpretation. If the comparison / baseline intervention does not have a convenient name (such as "Placebo"), some of these might be suitable:

• "Business as usual"

• "Treatment as usual"

• "No intervention"

(Whilst these may work well as short descriptions for outputting from this function, in your reporting you will still normally want to provide information about what exactly those in a comparison group got.)

This function is provided in the form of a convenience wrapper for confinterpret, using interpretations_equivalence as its interpretation_set.

Value

A list object of class interpretation_result with elements stating the interpretation in different formats, plus the parameters used to generate the interpretation.

Examples

# Establish a test confidence interval
ci_test <- matrix(c(-0.1, 0.1),
                  nrow = 1, dimnames = list("estimate",
                                            c("2.5 %", "97.5 %")))
interpret_equivalence(ci_test, 0, 0.2, c("Treatment as usual", "New treatment"))