Package 'sp23design' reference manual

Title:	Design and Simulation of Seamless Phase II-III Clinical Trials
Description:	Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Authors:	Balasubramanian Narasimhan [aut, cre], Mei-Chiung Shih [aut], Pei He [aut]
Maintainer:	Balasubramanian Narasimhan <[email protected]>
License:	LGPL-3
Version:	0.9-1
Built:	2024-10-25 03:15:40 UTC
Source:	https://github.com/cran/sp23design

sp23design: A package for the design, exploration and execution of seamless Phase II-II clinical trials

Description

This package implements the methodology described in the paper below

Details

Package:	sp23design
Type:	Package
Version:	1.0
Date:	2011-05-05
License:	LGPL?
LazyLoad:	yes

The most important functions in this package are generateSP23Design, exploreSP23Design, executeSP23Design, and analyzeSP23Design

Author(s)

Mei-Chiung Shih, Balasubramanian Narasimhan, Pei He

Maintainer: Balasubramanian Narasimhan <[email protected]>

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: 
  catn <- function(...) cat(..., "\n")

  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)
  print(sp23Design)
  trialHistory <- exploreSP23Design(sp23Design, numberOfSimulations=25, rngSeed=rngSeed)
  result <- analyzeSP23Design(sp23Design, trialHistory)$designSummary
  catn("numberOfTimesH0RIsRejectedAtFirstLook", result[["numberOfTimesH0RIsRejectedAtFirstLook"]])
  catn("numberOfTimesH0RIsRejected", result[["numberOfTimesH0RIsRejected"]])
  catn("numberOfTimesStoppedForFutility", result[["numberOfTimesStoppedForFutility"]])
  catn("numberOfTimesH0SIsAccepted", result[["numberOfTimesH0SIsAccepted"]])
  catn("numberOfTimesH0SIsRejected", result[["numberOfTimesH0SIsRejected"]])
  catn("numberOfTimesFutilityDecidedAtLastLook",result[["numberOfTimesFutilityDecidedAtLastLook"]])
  catn("numberOfTimesTrialEndedAtLook", result[["numberOfTimesTrialEndedAtLook"]])
  catn("avgExitTime", result[["avgExitTime"]])

## End(Not run)
## Not run: 
  catn <- function(...) cat(..., "\n")

  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)
  print(sp23Design)
  trialHistory <- exploreSP23Design(sp23Design, numberOfSimulations=25, rngSeed=rngSeed)
  result <- analyzeSP23Design(sp23Design, trialHistory)$designSummary
  catn("numberOfTimesH0RIsRejectedAtFirstLook", result[["numberOfTimesH0RIsRejectedAtFirstLook"]])
  catn("numberOfTimesH0RIsRejected", result[["numberOfTimesH0RIsRejected"]])
  catn("numberOfTimesStoppedForFutility", result[["numberOfTimesStoppedForFutility"]])
  catn("numberOfTimesH0SIsAccepted", result[["numberOfTimesH0SIsAccepted"]])
  catn("numberOfTimesH0SIsRejected", result[["numberOfTimesH0SIsRejected"]])
  catn("numberOfTimesFutilityDecidedAtLastLook",result[["numberOfTimesFutilityDecidedAtLastLook"]])
  catn("numberOfTimesTrialEndedAtLook", result[["numberOfTimesTrialEndedAtLook"]])
  catn("avgExitTime", result[["avgExitTime"]])

## End(Not run)

Analyses the results of running a design. If a trial history, such as the result of the function `executeSP23Design` or a history of the actual conduct of a single trial is provided, it returns the analysis results.

Description

Produces analysis results from the run of a single trial or a number of simulations.

Usage

analyzeSP23Design(sp23Design, trialHistory = NULL, data = NULL,
  col=c("red", "red", "brown", "brown"), lty=c(1,2,1,2))
analyzeSP23Design(sp23Design, trialHistory = NULL, data = NULL,
  col=c("red", "red", "brown", "brown"), lty=c(1,2,1,2))

Arguments

`sp23Design`	The design object typically produced by calling `generateSP23Design`.
`trialHistory`	Typically the result of calling `executeSP23Design` which is a single data frame, or, the result produced by calling `exploreSP23Design` which is a list of data frames, one for each simulation.
`data`	This is only used when the argument `trialHistory` is a single data frame, in which case, it should be the data generated in the clinical trial.
`col`	Colors used for the survival plots
`lty`	Line types for the survival plots

Details

If trialHistory is a single data frame, the naive response estimates and a survival plot are produced. Otherwise, the counts of the number of times the various hypothesis are rejected and other details are returned.

Value

A list of two items named responseSummary and designSummary If trialHistory is a single data frame, the naive response estimates are returned in responseSummary and a survival plot is produced. Otherwise, the counts of the number of times the various hypothesis are rejected and other details are returned in designSummary.

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: 
  catn <- function(...) cat(..., "\n")

  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)
  print(sp23Design)
  trialHistory <- exploreSP23Design(sp23Design, numberOfSimulations=25, rngSeed=rngSeed)
  result <- analyzeSP23Design(sp23Design, trialHistory)$designSummary
  catn("numberOfTimesH0RIsRejectedAtFirstLook",result[["numberOfTimesH0RIsRejectedAtFirstLook"]])
  catn("numberOfTimesH0RIsRejected", result[["numberOfTimesH0RIsRejected"]])
  catn("numberOfTimesStoppedForFutility", result[["numberOfTimesStoppedForFutility"]])
  catn("numberOfTimesH0SIsAccepted", result[["numberOfTimesH0SIsAccepted"]])
  catn("numberOfTimesH0SIsRejected", result[["numberOfTimesH0SIsRejected"]])
  catn("numberOfTimesFutilityDecidedAtLastLook",result[["numberOfTimesFutilityDecidedAtLastLook"]])
  catn("numberOfTimesTrialEndedAtLook", result[["numberOfTimesTrialEndedAtLook"]])
  catn("avgExitTime", result[["avgExitTime"]])

## End(Not run)

## Not run: 
  catn <- function(...) cat(..., "\n")

  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)
  print(sp23Design)
  trialHistory <- exploreSP23Design(sp23Design, numberOfSimulations=25, rngSeed=rngSeed)
  result <- analyzeSP23Design(sp23Design, trialHistory)$designSummary
  catn("numberOfTimesH0RIsRejectedAtFirstLook",result[["numberOfTimesH0RIsRejectedAtFirstLook"]])
  catn("numberOfTimesH0RIsRejected", result[["numberOfTimesH0RIsRejected"]])
  catn("numberOfTimesStoppedForFutility", result[["numberOfTimesStoppedForFutility"]])
  catn("numberOfTimesH0SIsAccepted", result[["numberOfTimesH0SIsAccepted"]])
  catn("numberOfTimesH0SIsRejected", result[["numberOfTimesH0SIsRejected"]])
  catn("numberOfTimesFutilityDecidedAtLastLook",result[["numberOfTimesFutilityDecidedAtLastLook"]])
  catn("numberOfTimesTrialEndedAtLook", result[["numberOfTimesTrialEndedAtLook"]])
  catn("avgExitTime", result[["avgExitTime"]])

## End(Not run)

Given the estimates of the $\pi$ and $\theta$ , compute $d$ .

Description

This function computes $d$ via

$d(\pi,\xi)=\{ \pi_0 a + (1-\pi_0)\} - \{\pi_1 abc + (1-\pi_1)b \}$

Usage

computeDGivenXi(piVec, xiVec)
computeDGivenXi(piVec, xiVec)

Arguments

`piVec`	The two-element vector of $(\pi_0, \pi_1)$
`xiVec`	The three-element vector of $(a=e^\alpha, b=e^\beta, c=e^\gamma)$

Details

This is an approximation to the hazard ratio

Value

The computed value of $d$ , a scalar

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.

Compute the estimate of the variance of the generalized likelihood ratio statistic at time $t$

Description

This function computes the variance of the generalized likelihood ratio statistic at interim stopping times

Usage

computeGammaSubT(thetaHat, pi, interimData)
computeGammaSubT(thetaHat, pi, interimData)

Arguments

`thetaHat`	The three-element vector of $(\alpha, \beta, \gamma)$
`pi`	The two-element vector of $(\pi_0, \pi_1)$
`interimData`	The interim data at time $t$ as a data frame

Details

The function builds a hessian matrix and uses a reparametrization to compute $\Gamma_t$ , the variance of the generalized likelihood ration stochastic process at time $t$.

Value

A scalar value of the variance $\Gamma_t$

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.

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)
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)
## 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)

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)
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)
## 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)

Explore a specified design by simulation

Description

Explore a chosen design by generating simulated datasets and storing data from simulations for further analysis

Usage

exploreSP23Design(sp23Design, numberOfSimulations = 25, rngSeed = 12345,
showProgress = TRUE)
exploreSP23Design(sp23Design, numberOfSimulations = 25, rngSeed = 12345,
showProgress = TRUE)

Arguments

`sp23Design`	A design object typically created by the function `generateSP23Design`
`numberOfSimulations`	The number of simulations to use, default 25
`rngSeed`	A seed for the random number generator for reproducibility
`showProgress`	A flag (default `TRUE`) to show progress or not

Details

This function is used while exploring the characteristics of a design. Results are accumulated and can be fed into analysis functions to inform choices.

Value

A list of length numberOfSimulations where each entry is a data frame with number of interim looks rows and the following variables in the column.

`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, 2011, (submitted).

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)

  ## Case C of table 1 in paper
  caseC.TrueParameters <- list(p0 = 0.3,
                               p1 = 0.6,
                               pdiffHyp=0.3,
                               theta = list(
                                   alpha = 0,
                                   beta = 0,
                                   gamma = 0),
                               baselineLambda = 0.35,
                               etaHyp = 0.25)
  ## Do case C as example
  sp23Design <- generateSP23Design(caseC.TrueParameters, trialParameters)
  trialHistory <- exploreSP23Design(sp23Design, numberOfSimulations=25, rngSeed=2387487)

## End(Not run)
## 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)

  ## Case C of table 1 in paper
  caseC.TrueParameters <- list(p0 = 0.3,
                               p1 = 0.6,
                               pdiffHyp=0.3,
                               theta = list(
                                   alpha = 0,
                                   beta = 0,
                                   gamma = 0),
                               baselineLambda = 0.35,
                               etaHyp = 0.25)
  ## Do case C as example
  sp23Design <- generateSP23Design(caseC.TrueParameters, trialParameters)
  trialHistory <- exploreSP23Design(sp23Design, numberOfSimulations=25, rngSeed=2387487)

## End(Not run)

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)
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)

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)

Generate interim data for a clinical trial from a data set.

Description

Generate interim data at a given time from a dataset

Usage

generateInterimData(clinicalTrialDF, interimTime, administrativeCensoringTime)
generateInterimData(clinicalTrialDF, interimTime, administrativeCensoringTime)

Arguments

`clinicalTrialDF`	The data frame from which to generate the interim data. It is assumed that the variables `entryTime`, `responseIndicator`, `treatmentIndicator` and `timeToEvent` are present
`interimTime`	the interim time for which the data is to generated
`administrativeCensoringTime`	The administrative censoring time when the study concludes

Details

As it stands this function also is geared towards the simulation scenario. Needs to be cleaned up a bit.

Value

Returns a subset of the input data frame with the following additional variables.

`delta`	the event indicator
`eventTime`	calendar event time

Furthermore, the timeToEvent variable is appropriately calculated

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)
  dInterim <- generateInterimData(d, trialParameters$interimLookTime[2],
                                     trialParameters$adminCensoringTime)

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)

Generate a seamless Phase II-III design object given some the true parameters and clinical trial parameters

Description

Generate a seamless Phase II-III design object given some the true parameters and clinical trial parameters

Usage

generateSP23Design(trueParameters, trialParameters)
generateSP23Design(trueParameters, trialParameters)

Arguments

trueParameters

A list constisting of several components including p0, the true probability of response under control, p1, the true probability of response under treatment, theta, a list of three items $(\alpha,\beta,\gamma)$ , baselineLambda, the base line hazard rate (constant for now), etaHyp, the hypothesized non-null hazard $d$ .

trialParameters

A list constisting of several components including numberRecruitedEachYear, a vector of recruitment numbers for each year, interimLookTime, the calendar interim look times, followupTime, the follow-up time, adminCensoringTime, the administrative censoring time, glrBoundarySidedness, either one or two-sided generalized likelihood ratio boundaries, default one-sided, typeIError, the type I error desired, type2Error, the type II error desired, used only for computing futility boundaries (only nominally used; need to clarify), epsTypeI, the fraction to spend in interim looks for the modified Haybittle-Peto boundaries, epsTypeII, the fraction to spend in interim looks for the modified Haybittle-Peto boundaries.

Details

Generates a design object that is used throughout the simulation or an actual analysis.

Value

An informal sp23Design object, a list of four items

`trueParameters`	exactly the input above
`trialParameters`	exactly the input above
`glrBoundary`	a matrix of dimension number of interim looks by 4, containing the boundaries for futility and efficacy for both response and survival
`interimLookHistoryDF`	A data frame as described in `exploreSP23Design`.

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

## trial parameters in paper
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)

## Case C of table 1 in paper
caseC.TrueParameters <- list(p0 = 0.3,
                             p1 = 0.6,
                             pdiffHyp=0.3,
                             theta = list(
                               alpha = 0,
                               beta = 0,
                               gamma = 0),
                             baselineLambda = 0.35,
                             etaHyp = 0.25)


## Do case C as example
sp23Design <- generateSP23Design(caseC.TrueParameters, trialParameters)
## trial parameters in paper
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)

## Case C of table 1 in paper
caseC.TrueParameters <- list(p0 = 0.3,
                             p1 = 0.6,
                             pdiffHyp=0.3,
                             theta = list(
                               alpha = 0,
                               beta = 0,
                               gamma = 0),
                             baselineLambda = 0.35,
                             etaHyp = 0.25)


## Do case C as example
sp23Design <- generateSP23Design(caseC.TrueParameters, trialParameters)

A utility function to compute the hessian of the generalized (conditional) partial likelihood ratio statistic

Description

A utility function to compute the hessian of the generalized (conditional) partial likelihood ratio statistic

Usage

hessian(theta, pi, interimData)
hessian(theta, pi, interimData)

Arguments

`theta`	The three-element vector $(\alpha, \beta, \gamma)$
`pi`	The two-element vector $(\pi_0, \pi_1)$
`interimData`	The interim data frame

Details

Computes the hessian

Value

A 3 $\times$ 3 matrix of the hessian

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.

Compute the response log-likelihood

Description

Compute the response log-likelihood

Usage

loglik1(piVec, respSummary)
loglik1(piVec, respSummary)

Arguments

`piVec`	The two-element vector of $(\pi_0, \pi_1)$
`respSummary`	A vector consisting of the summary of data described in `computeResponseSummary`

Details

Computes the log-likelihood

Value

the log-likelihood

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.

Computes the constrained response log-likelihood (on the alternative)

Description

Computes the constrained response log-likelihood (on the alternative)

Usage

loglik1GivenDelta(p, respSummary, delta = 0)
loglik1GivenDelta(p, respSummary, delta = 0)

Arguments

`p`	The probability $\pi_0$
`respSummary`	A vector consisting of the summary of data described in `computeResponseSummary`
`delta`	The scalar value of the difference $\pi_1-\pi_0$

Details

Computes the constrained response log-likelihood (on the alternative)

Value

the (constrained) response log likelihood

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.

Computes the survival log-likelihood

Description

Computes the survival log-likelihood

Usage

loglik2(theta, interimData)
loglik2(theta, interimData)

Arguments

`theta`	the three-element vector of $(\alpha, \beta, \gamma)$
`interimData`	The interim data

Details

Computes the survival log-likelihood

Value

the survival log-likelihood

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.

Computes the constrained survival log-likelihood

Description

Computes the constrained survival log-likelihood

Usage

loglik2.repar0(xi, interimData, pi0, pi1, eta.hyp = 0)
loglik2.repar0(xi, interimData, pi0, pi1, eta.hyp = 0)

Arguments

`xi`	the three-element vector of $(a=e^\alpha, b=e^\beta, c=e^\gamma)$
`interimData`	the interim data
`pi0`	the value $\pi_0$
`pi1`	the value $\pi_1$
`eta.hyp`	The hypothesised difference $d$ in the alternative hypothesis

Details

This uses the reparametrization above in terms of $(a,b,c)$ rather than $(\alpha, \beta, \gamma )$

Value

The constrained survival log-likelihood

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.

Compute the modified Haybittle-Peto boundary

Description

Compute the modified Haybittle-Peto boundary

Usage

mHP.b(mu = c(0, 0), v = c(1, 2), alpha = 0.05, eps = 1/2, side = c("one", "two"))
mHP.b(mu = c(0, 0), v = c(1, 2), alpha = 0.05, eps = 1/2, side = c("one", "two"))

Arguments

`mu`	The mean vector
`v`	The variance vector, usually proportional to information in calendar time
`alpha`	The significance desired
`eps`	The fraction of alpha to use
`side`	one-sided or two-sided (`one` or `two`)

Details

Compute the modified Haybittle-Peto boundary

Value

the modified Haybittle-Peto boundary

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.

Compute the modified Haybittle-Peto boundary for the final look

Description

Compute the modified Haybittle-Peto boundary for the final look

Usage

mHP.c(mu = c(0, 0, 0), v = c(1, 2, 3), b = 3, alpha = 0.05,
      eps = 1/2, side = c("one", "two"))
mHP.c(mu = c(0, 0, 0), v = c(1, 2, 3), b = 3, alpha = 0.05,
      eps = 1/2, side = c("one", "two"))

Arguments

`mu`	the mean vector
`v`	The variance vector, usually proportional to information in calendar time
`b`	The (constant) modified Haybittle-Peto boundary, typically computed by `mHP.b`
`alpha`	The significance level desired
`eps`	The fraction of alpha to use
`side`	one-sided or two-sided (`one` or `two`)

Details

Compute the modified Haybittle-Peto boundary for the final look

Value

the modified Haybittle-Peto boundary for the final look

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.

Perform an interim look in a seamless Phase II-III clinical trial

Description

Perform an interim look in a seamless Phase II-III clinical trial

Usage

performInterimLook(k, trueParameters, trialParameters, glrBoundary,
                   interimData, interimLookHistoryDF, argRejectH0R)
performInterimLook(k, trueParameters, trialParameters, glrBoundary,
                   interimData, interimLookHistoryDF, argRejectH0R)

Arguments

`k`	the index of the look
`trueParameters`	the true parameters, usually available in an object generated by `generateSP23Design`
`trialParameters`	the trial parameters, usually available in an object generated by `generateSP23Design`
`glrBoundary`	the generalized likelihood ratio boundaries, usually available in an object produced by `generateSP23Design`
`interimData`	the interim data
`interimLookHistoryDF`	the interim look history data frame matrix described in `exploreSP23Design`
`argRejectH0R`	A flag that indicates whether $H_0^R$ has been rejected in the previous look; A value of `FALSE` is used for first look.

Details

Perform an interim look in a seamless Phase II-III clinical trial

Value

a vector of named values described in exploreSP23Design, essentially providing a new row to the interim look history data frame

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

  sp23Design <- generateSP23Design(trueParameters, trialParameters)
  d <- generateClinicalTrialData(nRec = trialParameters$numberRecruitedEachYear,
                                     nFUp = trialParameters$followupTime,
                                     pi0 = trueParameters$p0,
                                     pi1 = trueParameters$p1,
                                     theta = trueParameters$theta,
                                     lambda0 = trueParameters$baselineLambda)
  dInterim <- generateInterimData(d, trialParameters$interimLookTime[1],
                                      trialParameters$adminCensoringTime)
  dInterim <- dInterim[order(dInterim$timeToEvent), ]
  ## This is a tricky function to use for all but the first interim look;
  ## see executeSP23Design code for details! Reason: interim look k depends
  ## on results of interim look k-1
  ##
  performInterimLook(1, sp23Design$trueParameters, trialParameters, sp23Design$glrBoundary,
                     dInterim, sp23Design$interimLookHistoryDF,
                     argRejectH0R = FALSE)

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)
  dInterim <- generateInterimData(d, trialParameters$interimLookTime[1],
                                      trialParameters$adminCensoringTime)
  dInterim <- dInterim[order(dInterim$timeToEvent), ]
  ## This is a tricky function to use for all but the first interim look;
  ## see executeSP23Design code for details! Reason: interim look k depends
  ## on results of interim look k-1
  ##
  performInterimLook(1, sp23Design$trueParameters, trialParameters, sp23Design$glrBoundary,
                     dInterim, sp23Design$interimLookHistoryDF,
                     argRejectH0R = FALSE)

Reset the design object so that counts and results are zeroed out

Description

Reset the design object so that counts and results are zeroed out

Usage

resetSP23Design(sp23Design)
resetSP23Design(sp23Design)

Arguments

sp23Design

An object usually the result of generateSP23Design

Details

Reset the design object so that counts and results are zeroed out

Value

A new sp23Design object with counts and results zeroed out

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)

  ## Case C of table 1 in paper
  caseC.TrueParameters <- list(p0 = 0.3,
                               p1 = 0.6,
                               pdiffHyp=0.3,
                               theta = list(
                                   alpha = 0,
                                   beta = 0,
                                   gamma = 0),
                               baselineLambda = 0.35,
                               etaHyp = 0.25)


  ## Do case C as example
  sp23Design <- generateSP23Design(caseC.TrueParameters, trialParameters)
  ## do something ...
  sp23Design <- resetSP23Design(sp23Design)

## End(Not run)
## 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)

  ## Case C of table 1 in paper
  caseC.TrueParameters <- list(p0 = 0.3,
                               p1 = 0.6,
                               pdiffHyp=0.3,
                               theta = list(
                                   alpha = 0,
                                   beta = 0,
                                   gamma = 0),
                               baselineLambda = 0.35,
                               etaHyp = 0.25)


  ## Do case C as example
  sp23Design <- generateSP23Design(caseC.TrueParameters, trialParameters)
  ## do something ...
  sp23Design <- resetSP23Design(sp23Design)

## End(Not run)

A convenience function to reduce dimension by solving for one variable $c$ in terms of others $a$ , $b$ , $d$

Description

A convenience function to reduce dimension by solving for one variable $c$ in terms of others $a$ , $b$ , $d$

Usage

solveForCGivenABD(piVec, a, b, d)
solveForCGivenABD(piVec, a, b, d)

Arguments

`piVec`	The two-element vector of $(\pi_0, \pi_1)$
`a`	The value for $a$
`b`	The value for $b$
`d`	The value for $d$

Details

Just solves the equation in closed form

Value

the value for $c$

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.

Package 'sp23design'

Help Index

sp23design: A package for the design, exploration and execution of seamless Phase II-II clinical trials

Description

Details

Author(s)

References

Examples

Analyses the results of running a design. If a trial history, such as the result of the function executeSP23Design or a history of the actual conduct of a single trial is provided, it returns the analysis results.

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Given the estimates of the π\piπ and θ\thetaθ, compute ddd.

Description

Usage

Arguments

Details

Value

Author(s)

References

Compute the estimate of the variance of the generalized likelihood ratio statistic at time ttt

Description

Usage

Arguments

Details

Value

Author(s)

References

Given interim data, compute the response end-point summary

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

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

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Explore a specified design by simulation

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

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

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Generate interim data for a clinical trial from a data set.

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Generate a seamless Phase II-III design object given some the true parameters and clinical trial parameters

Description

Analyses the results of running a design. If a trial history, such as the result of the function `executeSP23Design` or a history of the actual conduct of a single trial is provided, it returns the analysis results.

Given the estimates of the $\pi$ and $\theta$ , compute $d$ .

Compute the estimate of the variance of the generalized likelihood ratio statistic at time $t$