interpret_superiority: Superiority test interpretations of confidence intervals.

In confinterpret: Descriptive Interpretations of Confidence Intervals

Description

Conduct superiority 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_superiority(ci, null_value = 0, 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.
null_value	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. For superiority tests this is the point value that the confidence interval is compared at.
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

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_superiority 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_superiority(ci_test, 0, c("Treatment as usual", "New treatment"))

confinterpret documentation built on May 1, 2019, 8:41 p.m.