test.NI.continuous: Non-inferiority testing with continuous outcome.

In Matteo21Q/dani: Design and Analysis of Non-Inferiority Trials

Non-inferiority testing with continuous outcome.

Description

Functions for testing non-inferiority when the outcome is continuous. It allows for a number of tests on different summary measures: difference of means or ratio of means.

Usage

  test.NI.continuous(y.control=NULL, y.experim=NULL,  NI.margin, sig.level=0.025, 
                               summary.measure="mean.difference", 
                               formula=NULL, data=NULL, control.level=0,
                               print.out=TRUE, higher.better=TRUE, test.type=NULL,
                               M.boot=2000, bootCI.type="bca", sd.control=NULL, 
                               sd.experim=NULL)  

Arguments

y.control	Vector of measurements in the control arm.
y.experim	Vector of measurements in the experimental arm.
NI.margin	Non-inferiority margin, expressed as the specified summary measure.
sig.level	One-sided significance level for testing. Default is 0.025, i.e. 2.5%.
summary.measure	The population-level summary measure to be estimated, i.e. the scale on which we define the non-inferiority margin. Can be one of "mean.difference" (Difference of Means) or "mean.ratio" (Ratio of Means).
formula	The formula for the outcome model. The variable indicating treatment has to be put within brackets and preceded by treat, e.g. treat(treatment).
data	A data.frame with all data.
control.level	Defines the control level in the treatment variable when using the formula and data interface. Defaults to 0.
print.out	Logical. If FALSE, no output is printed.
higher.better	Logical. If FALSE, the outcome is considered unfavourable, i.e. higher scores indicate worse outcomes. Default is TRUE, i.e. favourable outcome, higher scores indicate better outcomes.
test.type	A character string defining the method to be used for calculation of the confidence interval. For the mean difference, methods available include "Z.test", "t.test" or bootstrap based on 3 different types of confidence intervals: "bootstrap.basic", "bootstrap.bca" or "bootstrap.percentile". For the mean ratio, methods available include "Fiellers" test, "lm" (marginalisation after using linear regression) or the three methods using bootstrap: "bootstrap.basic", "bootstrap.bca" or "bootstrap.percentile".
M.boot	Number of bootstrap samples, e.g. for "bootstrap"" and "MUE.parametric.bootstrap" methods.
bootCI.type	Method for computing the confidence intervals if using a bootstrap method. It can be either "norm", "basic", "perc" or "bca". Default is "bca", which is the recommended option when possible.
sd.control	The assumed standard deviation of the control arm if using a Z test.
sd.experim	The assumed standard deviation of the experimental arm if using a Z test.

Details

This is a function to test non-inferiority of an experimental treatment against the active control within a specific NI margin. The margin can be specified on a number of different summary measures, including mean difference, or mean ratio. It is possible to test both with favourable (e.g. cognitive score) or unfavourable (e.g. pain score) outcomes and using a multitude of methods taken from other packages. See the entry on the test.type argument for the specific methods available for each summary measure.

Value

The output is a list, containing the estimate, standard error, cofidence interval (two-sided 2*alpha level), Z statistic and p-value. Additionally, a non-inferiority indicator is included and an indicator of whether the p-value was precise or just estimated from the confidence interval using normal approximation.

Examples

  
y0<-rnorm(10,2)
y1<-rnorm(10,2)
NI.m=-0.75
alpha=0.025

set.seed(1)
out5A<-test.NI.continuous(y0, y1, NI.m, alpha, test.type="Z.test", sd.control=1, sd.experim = 1)

  

Matteo21Q/dani documentation built on Aug. 29, 2024, 12:48 a.m.