IntervalEstimatorclass {adestr}  R Documentation 
Interval estimators
Description
This is the parent class for all confidence intervals implemented in this package.
Currently, only confidence intervals for the parameter \mu
of a normal distribution
are implemented. Details about the methods for calculating confidence intervals can be found in
(our upcoming paper).
Usage
IntervalEstimator(two_sided, l1, u1, l2, u2, label)
RepeatedCI(two_sided = TRUE)
StagewiseCombinationFunctionOrderingCI(two_sided = TRUE)
MLEOrderingCI(two_sided = TRUE)
LikelihoodRatioOrderingCI(two_sided = TRUE)
ScoreTestOrderingCI(two_sided = TRUE)
NeymanPearsonOrderingCI(two_sided = TRUE, mu0 = 0, mu1 = 0.4)
NaiveCI(two_sided = TRUE)
Arguments
two_sided 
logical indicating whether the confidence interval is twosided. 
l1 
functional representation of the lower boundary of the interval in the early futility and efficacy regions. 
u1 
functional representation of the upper boundary of the interval in the early futility and efficacy regions. 
l2 
functional representation of the lower boundary of the interval in the continuation region. 
u2 
functional representation of the upper boundary of the interval in the continuation region. 
label 
name of the estimator. Used in printing methods. 
mu0 
expected value of the normal distribution under the null hypothesis. 
mu1 
expected value of the normal distribution under the null hypothesis. 
Details
The implemented confidence intervals are:

MLEOrderingCI()

LikelihoodRatioOrderingCI()

ScoreTestOrderingCI()

StagewiseCombinationFunctionOrderingCI()
These confidence intervals are constructed by specifying an ordering of the sample space
and finding the value of \mu
, such that the observed sample is the
\alpha/2
(or (1\alpha/2
)) quantile of the sample space according to the
chosen ordering.
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).
Value
an object of class IntervalEstimator
. This class signals that an
object can be supplied to the evaluate_estimator
and the
analyze
functions.
References
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:10.1007/9783319325620.
See Also
Examples
# This is the definition of the 'naive' confidence interval for onearmed trials
IntervalEstimator(
two_sided = TRUE,
l1 = \(smean1, n1, sigma, ...) smean1  qnorm(.95, sd = sigma/sqrt(n1)),
u1 = \(smean1, n1, sigma, ...) smean1 + qnorm(.95, sd = sigma/sqrt(n1)),
l2 = \(smean1, smean2, n1, n2, sigma, ...) smean2  qnorm(.95, sd = sigma/sqrt(n1 + n2)),
u2 = \(smean1, smean2, n1, n2, sigma, ...) smean2 + qnorm(.95, sd = sigma/sqrt(n1 + n2)),
label="My custom CI")