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

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

Non-inferiority testing with survival outcome.

Description

Functions for testing non-inferiority when the outcome is survival (time-to-event). It allows for a number of tests on different summary measures: Hazard Ratio, Difference in Restricted Mean Survival Time and Difference in Survival.

Usage

  test.NI.survival(time, event, treat, covariates=NULL, NI.margin, sig.level=0.025, summary.measure="HR", 
                   print.out=TRUE, unfavourable=TRUE, test.type=NULL,
                   M.boot=2000, bootCI.type="bca", tau=NULL, control.level=NULL,
                             k=2, knots=NULL, bknots=NULL)  

Arguments

time	A vector with the event (or censoring) times.
event	A vector with the event indicators.
treat	A vector with treatment indicators.
covariates	Optional: a data.frame including all baseline covariates to be adjusted for.
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 "HR" (Hazard Ratio), "DRMST" (Difference in Resticted Mean Survival Time by time tau), or "DS" (Difference in Surviving proportion at time tau).
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 hazard ratio, methods available include "Cox.PH" (simple Cox proportional hazards model), "flexsurv.PH" (flexible parametric regression model) or "Cox.weighted" (Weighted Cox regression model, using Prentice weights). For "DRMST", possible methods are "KM" (Kaplan Meier), "Cox.PH.bootstrap" (bootstrap on estimates from Cox), "flexsurv.PH.delta" (delta method on estimates from flexible parametric survival model), "flexsurv.PH.bootstrap" (bootstrap on estimates from flexible parametric survival model). For "DS", methods available include "KM" (Kaplan Meier), "Cox.PH.bootstrap", "flexsurv.PH.delta", "flexsurv.PH.bootstrap".
M.boot	Number of bootstrap samples.
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.
k	If using flexsurv methods, this is the number of knots in the spline. The default k=0 gives a Weibull model. k is equivalent to df-1 in the notation of stpm for Stata. The knots are then chosen as equally-spaced quantiles of the log uncensored survival times, for example, at the median with one knot, or at the 33% and 67% quantiles of log time (or time, see "timescale") with two knots. To override this default knot placement, specify knots instead.
knots	If using flexsurv methods, Locations of knots on the axis of log time. If not specified, knot locations are chosen as described in k above. Either k or knots must be specified. If both are specified, knots overrides k.
bknots	If using flexsurv methods, this is the location of boundary knots, on the axis of log time. If not supplied, these are are chosen as the minimum and maximum log event time.
tau	The horizon time tau if using either "DRMST" or "DS" as summary measures.
control.level	A character variable specifying the level of the factor variable for treatment that has to be considered as the control one. If missing, the automatic reference will be assumed to be the control and a NOTE is printed on screen specifying which one that is.

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 HR, DRMST and difference in survival. It is possible to test both with favourable (e.g. cure) or unfavourable (e.g. death) outcomes and using different analysis methods. 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, confidence interval (two-sided 2*alpha level) 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

  
time1<-rnorm(200,100,10)
event1<-rbinom(200,1,0.5)
treat1<-rep(0:1,each=100)
out5A<-test.NI.survival(time=time1, event=event1, treat=treat1, NI.margin=1.5)

  

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