gsDesign-package: 1.0 Group Sequential Design

Description Details Author(s) References See Also Examples

Description

gsDesign is a package for deriving and describing group sequential designs. The package allows particular flexibility for designs with alpha- and beta-spending. Many plots are available for describing design properties.

Details

Package: gsDesign
Version: 2
License: GPL (version 2 or later)

Index:

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gsDesign                2.1: Design Derivation
gsProbability           2.2: Boundary Crossing Probabilities
plot.gsDesign           2.3: Plots for group sequential designs
gsCP                    2.4: Conditional Power Computation
gsBoundCP               2.5: Conditional Power at Interim Boundaries
gsbound                 2.6: Boundary derivation - low level
normalGrid              3.1: Normal Density Grid
binomial                3.2: Testing, Confidence Intervals and Sample Size
                             for Comparing Two Binomial Rates
Survival sample size    3.3: Time-to-event sample size calculation
                             (Lachin-Foulkes)
Spending function overview      4.0: Spending functions
sfHSD                   4.1: Hwang-Shih-DeCani Spending Function
sfPower                 4.2: Kim-DeMets (power) Spending Function
sfExponential           4.3: Exponential Spending Function
sfLDPocock              4.4: Lan-DeMets Spending function overview
sfPoints                4.5: Pointwise Spending Function
sfLogistic              4.6: 2-parameter Spending Function Families
sfTDist                 4.7: t-distribution Spending Function
Wang-Tsiatis Bounds     5.0: Wang-Tsiatis Bounds
checkScalar             6.0: Utility functions to verify variable properties

The gsDesign package supports group sequential clinical trial design. While there is a strong focus on designs using alpha- and beta-spending functions, Wang-Tsiatis designs, including O'Brien-Fleming and Pocock designs, are also available. The ability to design with non-binding futility rules allows control of Type I error in a manner acceptable to regulatory authorities when futility bounds are employed.

The routines are designed to provide simple access to commonly used designs using default arguments. Standard, published spending functions are supported as well as the ability to write custom spending functions. A gsDesign class is defined and returned by the gsDesign() function. A plot function for this class provides a wide variety of plots: boundaries, power, estimated treatment effect at boundaries, conditional power at boundaries, spending function plots, expected sample size plot, and B-values at boundaries. Using function calls to access the package routines provides a powerful capability to derive designs or output formatting that could not be anticipated through a gui interface. This enables the user to easily create designs with features they desire, such as designs with minimum expected sample size.

Thus, the intent of the gsDesign package is to easily create, fully characterize and even optimize routine group sequential trial designs as well as provide a tool to evaluate innovative designs.

Author(s)

Keaven Anderson

Maintainer: Keaven Anderson <[email protected]>

References

Jennison C and Turnbull BW (2000), Group Sequential Methods with Applications to Clinical Trials. Boca Raton: Chapman and Hall.

Proschan, MA, Lan, KKG, Wittes, JT (2006), Statistical Monitoring of Clinical Trials. A Unified Approach. New York: Springer.

See Also

gsDesign, gsProbability

Examples

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# assume a fixed design (no interim) trial with the same endpoint
# requires 200 subjects for 90% power at alpha=.025, one-sided
x <- gsDesign(n.fix=200)
plot(x)

keaven/gsDesign documentation built on Sept. 12, 2017, 9:24 p.m.