getCI | R Documentation |
Obtains the p-value, median unbiased point estimate, and confidence interval after the end of a group sequential trial.
getCI(
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_
)
L |
The termination look. |
zL |
The z-test statistic at the termination look. |
IMax |
The maximum information of the trial. |
informationRates |
The information rates up to look |
efficacyStopping |
Indicators of whether efficacy stopping is
allowed at each stage up to look |
criticalValues |
The upper boundaries on the z-test statistic scale
for efficacy stopping up to look |
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 |
A data frame with the following components:
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.
Kaifeng Lu, kaifenglu@gmail.com
Anastasios A. Tsiatis, Gary L. Rosner and Cyrus R. Mehta. Exact confidence intervals following a group sequential test. Biometrics 1984;40:797-803.
# 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))
# crossed the boundary at the second look
L = 2
n1 = n*2/3
delta1 = 7
sigma1 = 20
zL = delta1/sqrt(4/n1*sigma1^2)
# confidence interval
getCI(L = L, zL = zL, IMax = n/(4*sigma1^2),
informationRates = c(1/3, 2/3), alpha = 0.05,
typeAlphaSpending = "sfHSD", parameterAlphaSpending = -4)
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