getADCI_seamless: Confidence Interval After Adaptation for Phase 2/3 Seamless...

In lrstat: Power and Sample Size Calculation for Non-Proportional Hazards and Beyond

Confidence Interval After Adaptation for Phase 2/3 Seamless Design

Description

Obtains the p-value, conservative point estimate, and confidence interval after the end of an adaptive phase 2/3 seamless design.

Usage

getADCI_seamless(
  M = NA_integer_,
  r = 1,
  corr_known = TRUE,
  L = NA_integer_,
  zL = NA_real_,
  IMax = NA_real_,
  K = NA_integer_,
  informationRates = NA_real_,
  efficacyStopping = NA_integer_,
  criticalValues = NA_real_,
  alpha = 0.25,
  typeAlphaSpending = "sfOF",
  parameterAlphaSpending = NA_real_,
  spendingTime = NA_real_,
  MullerSchafer = FALSE,
  Lc = NA_integer_,
  zLc = NA_real_,
  INew = NA_real_,
  informationRatesNew = NA_real_,
  efficacyStoppingNew = NA_integer_,
  typeAlphaSpendingNew = "sfOF",
  parameterAlphaSpendingNew = NA_real_,
  spendingTimeNew = NA_real_
)

Arguments

M	Number of active treatment arms in Phase 2.
r	Randomization ratio of each active arm to the common control in Phase 2.
corr_known	Logical. If TRUE, the correlation between Wald statistics in Phase 2 is derived from the randomization ratio r as r / (r + 1). If FALSE, a conservative correlation of 0 is used.
L	The interim adaptation look in Phase 3.
zL	The z-test statistic at the interim adaptation look of Phase 3.
IMax	Maximum information for the active arm versus the common control for the original trial. Must be provided.
K	Number of sequential looks in Phase 3.
informationRates	A numeric vector of information rates fixed before the trial. If unspecified, defaults to (1:(K+1)) / (K+1).
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 max z-test statistic scale for Phase 2 and the z-test statistics for the selected arm in Phase 3 for the primary trial. If missing, boundaries will be computed based on the specified alpha spending function.
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.
MullerSchafer	Whether to use the Muller and Schafer (2001) method for trial adaptation.
Lc	The termination look of the integrated trial.
zLc	The z-test statistic at the termination look of the integrated trial.
INew	The maximum information for the active arm versus the common control in the secondary trial.
informationRatesNew	The spacing of looks of the secondary trial.
efficacyStoppingNew	The indicators of whether efficacy stopping is allowed at each look of the secondary trial. 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. Defaults to missing, in which case, it is the same as informationRatesNew.

Details

If typeAlphaSpendingNew is "OF", "P", "WT", or "none", then informationRatesNew, efficacyStoppingNew, and spendingTimeNew must be of full length kNew, and informationRatesNew and spendingTimeNew must end with 1.

Value

A data frame with the following variables:

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

• thetahat: 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, [e-mail]

References

Ping Gao, Yingqiu Li. Adaptive multiple comparison sequential design (AMCSD) for clinical trials. Journal of Biopharmaceutical Statistics, 2024, 34(3), 424-440.

Examples

getADCI_seamless(
  M = 2, r = 1, corr_known = FALSE,
  L = 1, zL = -log(0.67) * sqrt(80 / 4),
  IMax = 120 / 4, K = 2, informationRates = c(1/3, 2/3, 1),
  alpha = 0.025, typeAlphaSpending = "OF",
  Lc = 2, zLc = -log(0.677) * sqrt(236 / 4), INew = 236 / 4)

lrstat documentation built on May 13, 2026, 9:06 a.m.