PValue-class {adestr} | R Documentation |
This is the parent class for all p-values implemented in this package. Details about the methods for calculating p-values can be found in (our upcoming paper).
PValue(g1, g2, label)
LinearShiftRepeatedPValue(wc1f = 0, wc1e = 1/2, wc2 = 1/2)
MLEOrderingPValue()
LikelihoodRatioOrderingPValue()
ScoreTestOrderingPValue()
StagewiseCombinationFunctionOrderingPValue()
NeymanPearsonOrderingPValue(mu0 = 0, mu1 = 0.4)
g1 |
functional representation of the p-value in the early futility and efficacy regions. |
g2 |
functional representation of the p-value in the continuation region. |
label |
name of the p-value. Used in printing methods. |
wc1f |
slope of futility boundary change. |
wc1e |
slope of efficacy boundary change. |
wc2 |
slope of c2 boundary change. |
mu0 |
expected value of the normal distribution under the null hypothesis. |
mu1 |
expected value of the normal distribution under the null hypothesis. |
The implemented p-values are:
MLEOrderingPValue()
LikelihoodRatioOrderingPValue()
ScoreTestOrderingPValue()
StagewiseCombinationFunctionOrderingPValue()
The p-values are calculated by specifying an ordering of the sample space calculating the probability that a random sample under the null hypothesis is larger than the observed sample. Some of the implemented orderings are based on the work presented in (Emerson and Fleming 1990), (Sections 8.4 in Jennison and Turnbull 1999), and (Sections 4.1.1 and 8.2.1 in Wassmer and Brannath 2016).
an object of class PValue
. This class signals that an
object can be supplied to the analyze
function.
Emerson SS, Fleming TR (1990).
“Parameter estimation following group sequential hypothesis testing.”
Biometrika, 77(4), 875–892.
doi:10.2307/2337110.
Jennison C, Turnbull BW (1999).
Group Sequential Methods with Applications to Clinical Trials, 1 edition.
Chapman and Hall/CRC., New York.
doi:10.1201/9780367805326.
Wassmer G, Brannath W (2016).
Group Sequential and Confirmatory Adaptive Designs in Clinical Trials, 1 edition.
Springer, Cham, Switzerland.
doi:10.1007/978-3-319-32562-0.
# This is the definition of a 'naive' p-value based on a Z-test for a one-armed trial
PValue(
g1 = \(smean1, n1, sigma, ...) pnorm(smean1*sqrt(n1)/sigma, lower.tail=FALSE),
g2 = \(smean1, smean2, n1, n2, ...) pnorm((n1 * smean1 + n2 * smean2)/(n1 + n2) *
sqrt(n1+n2)/sigma, lower.tail=FALSE),
label="My custom p-value")