samplesize.NIfrontier.binary: Sample size calculator for trials with binary outcomes...

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

Sample size calculator for trials with binary outcomes designed with Non-Inferiority Frontiers

Description

A function to estimate sample size of a trial designed with a non-inferiority frontier and analysed on either the risk difference, risk ratio, odds ratio or arc-sine difference scale.

Usage

samplesize.NIfrontier.binary(p.control.expected, p.experim.target=NULL, NI.frontier, sig.level=0.025,
                                     summary.measure="RD", print.out=TRUE, unfavourable=TRUE, 
                                     power=0.9, r=1, round=T, ltfu=0)

Arguments

p.control.expected	Expected event risk in the control arm.
p.experim.target	Target event risk in the experimental arm under which to power the trial.
NI.frontier	Non-inferiority frontier, a function whos eonly input should be the control event risk and that returns the NI margin for that risk expressed as the specified summary measure.
sig.level	One-sided significance level for testing. Default is 0.025, i.e. 2.5%.
power	Power of the trial, i.e. one minus type-II error of the study. Default is 0.9, i.e. 90%.
r	Allocation ratio, i.e. ratio between sample sizes in the active and control goups. Default is 1.
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.
round	A logical variable. If TRUE, sample sizes are rounded to the next integer, using the ceiling function. Otherwise, they are left unrounded.
ltfu	A numeric variable with the expected proportion of patients lost to follow-up. Default is 0.

Details

This function estimates sample size to acheiev certain power for a non-inferiority trial designed defining a non-inferiority frontier. The NI frontier can be defined using different sumamry measures. The method used for sample size calculation is "LRT".

Value

A data.frame with estimated powers at each control event risk.

Examples

NI.frontier.RD<-function(p) return(0.1)
out<-try(samplesize.NIfrontier.binary(p.control.expected=0.1, p.experim.target=0.1, 
                                        NI.frontier=NI.frontier.RD, sig.level = 0.025, 
                                        power = 0.9, r = 1, 
                                        summary.measure = "RD", print.out = TRUE, 
                                        unfavourable=T))

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