getRCI {lrstat}

Repeated confidence interval for group sequential design

Description

Obtains the repeated confidence interval for a group sequential trial.

Usage

getRCI(
  L = NA_integer_,
  zL = NA_real_,
  IMax = NA_real_,
  informationRates = NA_real_,
  efficacyStopping = NA_integer_,
  criticalValues = NA_real_,
  alpha = 0.025,
  typeAlphaSpending = "sfOF",
  parameterAlphaSpending = NA_real_,
  spendingTime = NA_real_
)

Arguments

L

The look of interest.

zL

The z-test statistic at the look.

IMax

The maximum information of the trial.

informationRates

The information rates up to look L.

efficacyStopping

Indicators of whether efficacy stopping is allowed at each stage up to look L. Defaults to true if left unspecified.

criticalValues

The upper boundaries on the z-test statistic scale for efficacy stopping up to look L.

alpha

The significance level. Defaults to 0.025.

typeAlphaSpending

The type of alpha spending. 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. Corresponds to Delta for "WT", rho for "sfKD", and gamma for "sfHSD".

spendingTime

The error spending time up to look L. Defaults to missing, in which case, it is the same as informationRates.

Value

A data frame with the following components:

• pvalue: Repeated p-value for rejecting the null hypothesis.

• thetahat: Point estimate of the parameter.

• cilevel: Confidence interval level.

• lower: Lower bound of repeated confidence interval.

• upper: Upper bound of repeated confidence interval.

Author(s)

Kaifeng Lu, [e-mail]

References

Christopher Jennison and Bruce W. Turnbull. Interim analyses: the repeated confidence interval approach (with discussion). J R Stat Soc Series B. 1989;51:305-361.

Examples

# 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))

# results at the second look
L = 2
n1 = n*2/3
delta1 = 7
sigma1 = 20
zL = delta1/sqrt(4/n1*sigma1^2)

# repeated confidence interval
getRCI(L = L, zL = zL, IMax = n/(4*sigma1^2),
       informationRates = c(1/3, 2/3), alpha = 0.05,
       typeAlphaSpending = "sfHSD", parameterAlphaSpending = -4)