test.ROCI.binary: Function to analyse a MAMS-ROCI (aka DURATIONS) randomised...

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

Function to analyse a MAMS-ROCI (aka DURATIONS) randomised trial.

Description

A function that can be used to analyse a MAMS-ROCI randomised trial with binary outcome using one of several possible analysis methods to find the minimum/maximum non-inferior arm.

Usage

test.ROCI.binary(formula=NULL, data=NULL, NI.margin, reference=max(treatment.levels),
                               se.method="bootstrap", treatment.levels=unique(treatment), summary.measure="RD", 
                               tr.model="FP2.fixed", M.boot=NULL, bootCI.type="bca", parallel="no", n.cpus=1, sig.level=0.025,
                               unfavourable=TRUE)

Arguments

formula	The formula for the outcome model. The variable indicating the treatment aspect to optimise has to be put within brackets and preceded by treat, e.g. treat(treatment).
data	A data.frame with all data.
NI.margin	The non-inferiority margin(s). It can be either a scalar, if the margin is the same for all treatment levels, or a vector of length equal to that of treatment.levels minus one. For summary.measure="target.risk", this represents the target probability that we want to achieve, and if given as a vector it has length equal to that of treatment.levels.
reference	The arm that should act as a reference. It must be one of the arms in treatment levels. Default is the largest value among treatment.levels.
se.method	Method for calculating standard errors around the treatment-response curve. Can be either "bootstrap" or "delta".
treatment.levels	A vector with all the treatment levels to be investigated. Default is the ones to which patients were randomised.
summary.measure	The population-level summary measure that defines the estimand. It can be either "RD" (Risk Difference), "RR" (Risk Ratio), "target.risk" (target probability) or "OR" (Odds Ratio).
tr.model	Model to use for the treatment-response curve. It can be one of: "FP1.classic" or "FP2.classic" (Multivariable Fractional Polynomials as in package mfp, with a maximum of either 1 or 2 powers), "FP1.fixed" or "FP2.fixed" (Fractional polynomials fixing the number of powers to exactly 1 or 2).
M.boot	Number of bootstrap samples if using se.method="bootstrap".
bootCI.type	Method for computing the confidence intervals if using se.method="bootstrap". It can be either "norm", "basic", "perc" or "bca". Default is "bca", which is the recommended option when possible.
parallel	If using bootstrap, this gives the chance to parallelise calculations. Use parallel="snow" with windows, or parallel="multicore" with Linux. See help page for the boot function.
n.cpus	If using bootstrap in parallel, this gives teh number of cpus to parallelise computations on.
sig.level	One-sided significance level.
unfavourable	If TRUE the outcome is taken to be an unfavourable event, e.g. death or relapse. If FALSE, the outcome is considered a positive one, like cure.

Details

This function takes trial data on randomised treatment arm (e.g. duration) and associated binary outcome (e.g. 1-year death, or cure) and returns the estimated treatment-response curve. It estimates the chosen population-level summary measure (RD, RR or RR) either against the largest arm ( when minimisation=T) or the shortest one (when minimisation=F). It returns what is the optimum level of treatment according to trial results as well. Results can then be explored and visualised with summary.durations and plot.durations.

Value

A list including the model fit, the optimal treatment level, mean estimates and upper and lower bounds of the CI for the summary measure on the primary outcome at all treatment levels against control. Additionally, the list includes inputs such as summary measure, analysis method, non-inferiority margin and treatment levels.

Examples

duration.arms=c(8,10,12,14,16,18,20)
sam.sizes=c(700)
NI.margin.RD<-0.1

durations<-rep(duration.arms, each=100)
y<-rbinom(sam.sizes,1,0.05+(20-durations)*0.01)

data.ex<-data.frame(y,durations)
myformula<-as.formula(y~treat(durations))

res1<-test.ROCI.binary(formula=myformula, data=data.ex, 
                        se.method="delta", treatment.levels=8:20, 
                        summary.measure="RD", NI.margin=NI.margin.RD)

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