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Confidence Interval After Adaptation for Phase 2/3 Seamless Design

Source: R/RcppExports.R
getADCI_seamless.Rd

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.

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.

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.

References

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

Author

Kaifeng Lu, kaifenglu@gmail.com

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)
#>        pvalue  thetahat cilevel      lower     upper
#> 1 0.008471387 0.2557151    0.95 0.05573209 0.4420563