generateClinicalTrialData {sp23design}

A function to generate some clinical trial data according the joint model of response and survival for simulations

Description

The data is generated according to the model specified in the reference below, specifically,

\lambda(t \mid Y,Z)=\lambda_0(t) \exp(\alpha Y + \beta Z + \gamma Y Z)

Usage

generateClinicalTrialData(nRec, nFUp, pi0, pi1, theta, lambda0, blockSize = 10)

Arguments

nRec

the number of patients recruited every year. Length(nRec) is the number of years of recruitment

nFUp

the number of additional years of followup

pi0

the probability of response under control arm

pi1

the probability of response under treatment arm

theta

the three dimensional parameter (\alpha, \beta, \gamma) of the joint response/survival model

lambda0

the baseline hazard rate

blockSize

the size of the blocks for randomization of the treatment/control; we use block randomization

Details

Generates data from an exponentail distribution according to the model and adhering to the recruitment goals for each calendar year

Value

A data frame consisting of the following variables.

entryTime

entry time of the patient into the trial

responseIndicator

an indicator of patient being a responder or not

treatmentIndicator

an indicator of patient being in treatment arm or control

timeToEvent

the time to event or death in the language of the paper

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

  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)