R: Confidence interval after adaptation

getADCI {lrstat}

R Documentation

Confidence interval after adaptation

Description

Obtains the p-value, median unbiased point estimate, and confidence interval after the end of an adaptive trial.

Usage

getADCI(
  L = NA_integer_,
  zL = NA_real_,
  IMax = NA_real_,
  kMax = NA_integer_,
  informationRates = NA_real_,
  efficacyStopping = NA_integer_,
  criticalValues = NA_real_,
  alpha = 0.25,
  typeAlphaSpending = "sfOF",
  parameterAlphaSpending = NA_real_,
  spendingTime = NA_real_,
  L2 = NA_integer_,
  zL2 = NA_real_,
  INew = NA_real_,
  MullerSchafer = 0L,
  informationRatesNew = NA_real_,
  efficacyStoppingNew = NA_integer_,
  typeAlphaSpendingNew = "sfOF",
  parameterAlphaSpendingNew = NA_real_,
  spendingTimeNew = NA_real_
)

Arguments

`L`	The interim adaptation look of the primary trial.
`zL`	The z-test statistic at the interim adaptation look of the primary trial.
`IMax`	The maximum information of the primary trial.
`kMax`	The maximum number of stages of the primary trial.
`informationRates`	The information rates of the primary trial.
`efficacyStopping`	Indicators of whether efficacy stopping is allowed at each stage of the primary trial. Defaults to true if left unspecified.
`criticalValues`	The upper boundaries on the z-test statistic scale for efficacy stopping for the primary trial.
`alpha`	The significance level of the primary trial. Defaults to 0.025.
`typeAlphaSpending`	The type of alpha spending for the primary trial. One of the following: "OF" for O'Brien-Fleming boundaries, "P" for Pocock boundaries, "WT" for Wang & Tsiatis boundaries, "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function, and "none" for no early efficacy stopping. Defaults to "sfOF".
`parameterAlphaSpending`	The parameter value of alpha spending for the primary trial. Corresponds to Delta for "WT", rho for "sfKD", and gamma for "sfHSD".
`spendingTime`	The error spending time of the primary trial. Defaults to missing, in which case, it is the same as `informationRates`.
`L2`	The termination look of the secondary trial.
`zL2`	The z-test statistic at the termination look of the secondary trial.
`INew`	The maximum information of the secondary trial.
`MullerSchafer`	Whether to use the Muller and Schafer (2001) method for trial adaptation.
`informationRatesNew`	The spacing of looks of the secondary trial up to look `L2`.
`efficacyStoppingNew`	The indicators of whether efficacy stopping is allowed at each look of the secondary trial up to look `L2`. Defaults to true if left unspecified.
`typeAlphaSpendingNew`	The type of alpha spending for the secondary trial. One of the following: "OF" for O'Brien-Fleming boundaries, "P" for Pocock boundaries, "WT" for Wang & Tsiatis boundaries, "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function, and "none" for no early efficacy stopping. Defaults to "sfOF".
`parameterAlphaSpendingNew`	The parameter value of alpha spending for the secondary trial. Corresponds to Delta for "WT", rho for "sfKD", and gamma for "sfHSD".
`spendingTimeNew`	The error spending time of the secondary trial up to look `L2`. Defaults to missing, in which case, it is the same as `informationRatesNew`.

Value

A data frame with the following variables:

pvalue: p-value for rejecting the null hypothesis.
thetahat: Median unbiased point estimate of the parameter.
cilevel: Confidence interval level.
lower: Lower bound of confidence interval.
upper: Upper bound of confidence interval.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

References

Ping Gao, Lingyun Liu and Cyrus Mehta. Exact inference for adaptive group sequential designs. Stat Med. 2013;32(23):3991-4005.

Examples


# original group sequential design with 90% power to detect delta = 6
delta = 6
sigma = 17
n = 282
(des1 = getDesign(IMax = n/(4*sigma^2), theta = delta, kMax = 3,
                  alpha = 0.05, typeAlphaSpending = "sfHSD",
                  parameterAlphaSpending = -4))

# interim look results
L = 1
n1 = n/3
delta1 = 4.5
sigma1 = 20
zL = delta1/sqrt(4/n1*sigma1^2)

t = des1$byStageResults$informationRates

# Muller & Schafer (2001) method to design the secondary trial:
des2 = adaptDesign(
  betaNew = 0.2, L = L, zL = zL, theta = 5,
  kMax = 3, informationRates = t,
  alpha = 0.05, typeAlphaSpending = "sfHSD",
  parameterAlphaSpending = -4,
  MullerSchafer = TRUE,
  kNew = 3, typeAlphaSpendingNew = "sfHSD",
  parameterAlphaSpendingNew = -2)

n2 = ceiling(des2$secondaryTrial$overallResults$information*4*20^2)
ns = round(n2*(1:3)/3)
 (des2 = adaptDesign(
   INew = n2/(4*20^2), L = L, zL = zL, theta = 5,
   kMax = 3, informationRates = t,
   alpha = 0.05, typeAlphaSpending = "sfHSD",
   parameterAlphaSpending = -4,
   MullerSchafer = TRUE,
   kNew = 3, informationRatesNew = ns/n2,
   typeAlphaSpendingNew = "sfHSD",
   parameterAlphaSpendingNew = -2))

# termination at the second look of the secondary trial
L2 = 2
delta2 = 6.86
sigma2 = 21.77
zL2 = delta2/sqrt(4/197*sigma2^2)

t2 = des2$secondaryTrial$byStageResults$informationRates[1:L2]

# confidence interval
getADCI(L = L, zL = zL,
        IMax = n/(4*sigma1^2), kMax = 3,
        informationRates = t,
        alpha = 0.05, typeAlphaSpending = "sfHSD",
        parameterAlphaSpending = -4,
        L2 = L2, zL2 = zL2,
        INew = n2/(4*sigma2^2),
        MullerSchafer = TRUE,
        informationRatesNew = t2,
        typeAlphaSpendingNew = "sfHSD",
        parameterAlphaSpendingNew = -2)

[Package lrstat version 0.2.6 Index]