lrstat (version 0.2.13)

getADCI: 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_
)

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.

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.

Author

Kaifeng Lu,

References

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

See Also

adaptDesign

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)