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gsDesign

The gsDesign package supports group sequential clinical trial design, largely as presented in the book Group Sequential Methods with Applications to Clinical Trials by Christopher Jennison and Bruce Turnbull (Chapman and Hall/CRC, 2000). An easy-to-use web interface to both enable usage without coding and to generate code to be able to reproduce the design.

While there is a strong focus on designs using α and β 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. Particular effort has gone into designs with time-to-event endpoints.

Most 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 plot function provides a wide variety of plots summarizing designs: boundaries, power, estimated treatment effect at boundaries, conditional power at boundaries, spending function plots, expected sample size plot, and B-values at boundaries.

While the main design functions, gsDesign() and gsSurv() have a complex output, the function gsBoundSummary() provides a simple summary of a design in a data frame that can be useful for printing in a document.

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.

Updates in late 2018 and early 2019 largely enabled by Metrum Research Group (Devin Pastoor, Harsh Baid, Jonathan Sidi). These include, but are not limited to, converting unit testing to use testthat package as well as developing the github web pages and implementing covrpage to document unit testing. Yilong Zhang implemented 3.1.1 continuous integration at github. 2020 collaborations with Cytel, Inc. increased unit testing coverage to > 80% from essential unit testing done long ago. Much earlier development, testing and documentation help were lead largely by Bill Constantine and Rich Calaway while they were with Revolution Computing. Thanks to John Lueders for his excellent and extensive collaboration building the Shiny app.

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Version

Install

install.packages('gsDesign')

Monthly Downloads

3,431

Version

3.2.0

License

GPL (>= 3)

Maintainer

Keaven Anderson

Last Published

March 13th, 2021

Functions in gsDesign (3.2.0)

gsBound

Boundary derivation - low level
gsBinomialExact

One-Sample Binomial Routines
gsDensity

Group sequential design interim density function
Spending_Function_Overview

4.0: Spending function overview
eEvents

Expected number of events for a time-to-event study
summary.gsDesign

Bound Summary and Z-transformations
gsDesign package overview

1.0 Group Sequential Design
gsBoundCP

Conditional Power at Interim Boundaries
checkLengths

Utility functions to verify variable properties
gsCP

Conditional and Predictive Power, Overall and Conditional Probability of Success
normalGrid

Normal Density Grid
plot.binomialSPRT

FUNCTION_TITLE
print.nSurvival

Time-to-event sample size calculation (Lachin-Foulkes)
print.nSurv

Advanced time-to-event sample size calculation
hGraph

Create multiplicity graphs using ggplot2
nNormal

Normal distribution sample size (2-sample)
sfHSD

Hwang-Shih-DeCani Spending Function
sfPower

Kim-DeMets (power) Spending Function
sfLogistic

Two-parameter Spending Function Families
sfExponential

Exponential Spending Function
sfLDOF

Lan-DeMets Spending function overview
xtable

xtable
sfTruncated

Truncated, trimmed and gapped spending functions
sfLinear

Piecewise Linear and Step Function Spending Functions
ciBinomial

Testing, Confidence Intervals, Sample Size and Power for Comparing Two Binomial Rates
plot.gsDesign

Plots for group sequential designs
gsDesign

Design Derivation
sequentialPValue

Sequential p-value computation
sfPoints

Pointwise Spending Function
gsProbability

Boundary Crossing Probabilities
sfTDist

t-distribution Spending Function
condPower

Sample size re-estimation based on conditional power