executeSP23Design: Given a design object, interim data and the current calendar...

In sp23design: Design and Simulation of Seamless Phase II-III Clinical Trials

Given a design object, interim data and the current calendar time, conduct the interim analysis for the time

Description

This function is designed to be used in the field. Assuming a particular design is chosen, it conducts the interim analysis for a specific calendar time and provides the means for deciding whether to stop for futility or efficacy.

Usage

executeSP23Design(sp23DesignObject, data, currentCalendarTime)

Arguments

sp23DesignObject	A seamless phase II-III design object, typically one produced by the generateSP23Design function
data	The interim data, something akin to that produced by the function generateInterimData
currentCalendarTime	The current calendar time of the interim data. This better be one of the specified interim looks, or the function stops.

Details

This function is designed to be used in the field, although there are parts of it that are currently problematic. For example, in the field, there is typically no inkling of a responder or non-responder and yet the function as it currently stands is too wedded to the simulation scenario. Not hard to fix though.

Value

A vector of quantities is returned.

m0	number on control arm
m1	number on treatment arm
y0	number of responses in control
y1	number of responses in treatment
pi0Hat	estimate of the proportion of responders among control
pi1Hat	estimate of the proportion of responders among treatment
pi0HatH0	estimate of the proportion of responders among control under H_0
pi1HatH0	estimate of the proportion of responders among control under H_0
pi0HatH1	estimate of the proportion of responders among control under H_1
pi1HatH1	estimate of the proportion of responders among control under H_1
glrRespH0	estimate of the generalized likelihood ratio statistic for response under H_0
glrRespH1	estimate of the generalized likelihood ratio statistic for response under H_1
glrSurvH0	estimate of the generalized likelihood ratio statistic for survival under H_0
glrSurvH1	estimate of the generalized likelihood ratio statistic for survival under H_1
alphaHat	the estimate of α
alphaHatH0	the estimate of α under H_0
alphaHatH1	the estimate of α under H_1
betaHat	the estimate of β
betaHatH0	the estimate of β under H_0
betaHatH1	the estimate of β under H_1
gammaHat	the estimate of γ
gammaHatH0	the estimate of γ under H_0
gammaHatH1	the estimate of γ under H_1
hazard	the estimate of d
v	the estimate of Γ_t
rejectH0R	a flag indicating if $H_0^R$ was rejected at the interim look
acceptH0R	a flag indicating if $H_0^R$ was accepted (futility) at the interim look
rejectH0S	a flag indicating if $H_0^S$ was rejected at the interim look
acceptH0S	a flag indicating if $H_0^S$ was accepted (futility) at the interim look
b.metas.Last	the last Haybittle-Peto boundary for the survival end-point, if computed

Author(s)

Mei-Chiung Shih, Balasubramanian Narasimhan, Pei He

References

Lai, Tze Leung and Lavori, Philip W. and Shih, Mei-Chiung. Sequential Design of Phase II-III Cancer Trials, Statistics in Medicine, Volume 31, issue 18, p.1944-1960, 2012.

Examples

## Not run: 
  trialParameters <- list(minimumNumberOfEvents = 20,
                              minimumIncreaseInV = 0.2,
                              numberRecruitedEachYear = c(80, 120, 160, 160),
                              followupTime = 3,
                              adminCensoringTime = 7,
                              interimLookTime = c(1, 2, 3, 5, 7),
                              type1ErrorForResponse = 0.05,
                              type2ErrorForResponse = 0.01,
                              glrBoundarySidedness = "one", # one sided or two-sided
                              type1Error = 0.05,
                              type2Error = 0.10,
                              epsType1 = 1/3,
                              epsType2 = 1/3)

  trueParameters <- list(p0 = 0.3,
                             p1 = 0.3,
                             pdiffHyp=0.3,
                             theta = list(
                                     alpha = 0,
                                     beta = 0,
                                     gamma = 0),
                             baselineLambda = 0.35,
                             etaHyp = 0.25)
  rngSeed <- 9872831

  sp23Design <- generateSP23Design(trueParameters, trialParameters)
  d <- generateClinicalTrialData(nRec = trialParameters$numberRecruitedEachYear,
                                 nFUp = trialParameters$followupTime,
                                 pi0 = trueParameters$p0,
                                 pi1 = trueParameters$p1,
                                 theta = trueParameters$theta,
                                 lambda0 = trueParameters$baselineLambda)
  result <- executeSP23Design(sp23Design, d, trialParameters$interimLookTime[2])

## End(Not run)

sp23design documentation built on April 19, 2022, 5:08 p.m.