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

executeSP23Design {sp23design}

R Documentation

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 `\alpha`
`alphaHatH0`	the estimate of `\alpha` under `H_0`
`alphaHatH1`	the estimate of `\alpha` under `H_1`
`betaHat`	the estimate of `\beta`
`betaHatH0`	the estimate of `\beta` under `H_0`
`betaHatH1`	the estimate of `\beta` under `H_1`
`gammaHat`	the estimate of `\gamma`
`gammaHatH0`	the estimate of `\gamma` under `H_0`
`gammaHatH1`	the estimate of `\gamma` under `H_1`
`hazard`	the estimate of `d`
`v`	the estimate of `\Gamma_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)

[Package sp23design version 0.9-1 Index]