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

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

Non-inferiority testing with binary outcome.

Description

Functions for testing non-inferiority when the outcome is binary. It allows for a number of tests on different summary measures: risk difference, risk ratio, odds ratio or arc-sine difference.

Usage

  test.NI.binary(n.control=NULL, n.experim=NULL, e.control=NULL, 
                 e.experim=NULL, formula=NULL, data=NULL, 
                 control.level=0, NI.margin, sig.level=0.025, 
                 summary.measure="RD", print.out=TRUE, 
                 unfavourable=TRUE, test.type=NULL, M.boot=2000,
                 bootCI.type="bca", BB.adj=0.0001, 
                 recursive.p.estim=FALSE) 

Arguments

e.control	Number of events in the control arm.
e.experim	Number of events in the active arm.
n.control	Total sample size of the control arm.
n.experim	Total sample size of the active arm.
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.
NI.margin	Non-inferiority margin, expressed as the specified summary measure.
control.level	Defines the control level in the treatment variable when using the formula and data interface. Defaults to 0.
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 "RD" (Risk Difference), "RR" (Risk Ratio), "OR" (Odds Ratio) or "AS" (Arc-Sine difference).
print.out	Logical. If FALSE, no output is printed.
unfavourable	Logical. If FALSE, the outcome is considered favourable, e.g. cure. Default is TRUE, i.e. unfavourable outcome, e.g. death.
test.type	A character string defining the method to be used for calculation of the confidence interval. For the risk difference, methods available include "Wald", "Wald.cc" (with continuity correction), "Hauck.Anderson", "Gart.Nam", "Newcombe10", "Newcombe11", "Haldane", "Jeffrey.Perks", "Agresti.Caffo", "Miettinen.Nurminen", "Farrington.Manning", "logistic" (marginalisation after fitting logistic regression model with glm), "bootstrap", "Agresti.Min", "Brown.Li.Jeffreys", "Chan.Zhang", "BLNM", "Mee", "uncond.midp", "Berger.Boos", "MUE.Lin", "MUE.parametric.bootstrap". For risk ratio, possible methods are "Wald.Katz", "adjusted.Wald.Katz", "inverse.hyperbolic.sine", "Koopman", "MOVER.R", "Miettinen.Nurminen", "MOVER", "Gart.Nam", "score.cc" (with continuity correction),"logregression" (binomial regression with log link), "bootstrap", "Bailey", "Noether", "Chan.Zhang", "Agresti.Min", "uncond.midp", "Berger.Boos". For odds ratio, methods available include "Wald.Woolf", "adjusted.Wald.Woolf", "inverse.hyperbolic.sine", "Cornfield.exact", "MOVER.R", "Miettinen.Nurminen", "MOVER", "Gart.Nam", "score.cc", "logistic" (logistic regression), "bootstrap", "Cornfield.midp", "Baptista.Pike.exact", "Baptista.Pike.midp", "Chan.Zhang", "Agresti.Min", "uncond.midp", "Berger.Boos". Finally, for arc-sine difference the only available emthod is "Wald".
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.
BB.adj	Adjustment factor for "Berger.Boos" method.
recursive.p.estim	If TRUE, the p value is estimated by recursively running the function with varied significance level until the NI margin is crossed. If FALSE (default), the p-value is either the one calculated with standard methods or an estimate based on normal approximation.

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 absolute risk difference, risk ratio, odds ratio and arc-sine difference. It is possible to test both with favourable (e.g. cure) or unfavourable (e.g. death) outcomes and using a multitude of methods taken from other packages. See the entry on the test.type argument for the specific methods availabel 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

  
n.control<-1000
n.experim<-1000
e.control<-0.05*n.control
e.experim<-0.05*n.experim
NImRD=0.05
NImRR=2
NImOR=1.7
NImAS=arcsine.margin(0.1, 0.05)
alpha=0.025

test1<-test.NI.binary(n.control=n.control, n.experim=n.experim, e.control=e.control, e.experim=e.experim, NI.margin=NImRD, sig.level=alpha, summary.measure = "RD")
test1b<-test.NI.binary(n.control=n.control, n.experim=n.experim, e.control=e.control, e.experim=e.experim, NI.margin=NImRD, sig.level=alpha, summary.measure = "RD", test.type="Miettinen.Nurminen")
test2<-test.NI.binary(n.control=n.control, n.experim=n.experim, e.control=e.control, e.experim=e.experim, NI.margin=NImRR, sig.level=alpha, summary.measure = "RR")
test3<-test.NI.binary(n.control=n.control, n.experim=n.experim, e.control=e.control, e.experim=e.experim, NI.margin=NImOR, sig.level=alpha, summary.measure = "OR", test.type="inverse.hyperbolic.sine")
test4<-test.NI.binary(n.control=n.control, n.experim=n.experim, e.control=e.control, e.experim=e.experim, NI.margin=NImAS, sig.level=alpha, summary.measure = "AS")

  

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