computeResponseSummary {sp23design}

Given interim data, compute the response end-point summary

Description

Compute the response end-point summary for interim data. This will include the proportion of responses, the proportion of people on treatment and control etc.

Usage

computeResponseSummary(interimData)

Arguments

interimData

The interim data for the clinical trial

Details

The result is a vector of counts and proportions

Value

m0

number on control arm

m1

number on treatment arm

y0

number of responses in control

y1

number of responses in treatment

numberOfTotalResponses

number of total responses in both arms

controlRespProp

the proportion of responders in control arm

treatmentRespProp

the proportion of responders in the treatment arm

pooledProp

the pooled response proportion

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

  d <- generateClinicalTrialData(nRec = trialParameters$numberRecruitedEachYear,
                                     nFUp = trialParameters$followupTime,
                                     pi0 = trueParameters$p0,
                                     pi1 = trueParameters$p1,
                                     theta = trueParameters$theta,
                                     lambda0 = trueParameters$baselineLambda)
  dInterim <- generateInterimData(d, trialParameters$interimLookTime[2],
                                      trialParameters$adminCensoringTime)
  computeResponseSummary(dInterim)

## End(Not run)