Learn R Programming

⚠️There's a newer version (3.6.4) of this package.Take me there.

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% in version 3.2.0 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.

Copy Link

Version

Install

install.packages('gsDesign')

Monthly Downloads

1,793

Version

3.2.1

License

GPL (>= 3)

Maintainer

Last Published

July 12th, 2021

Functions in gsDesign (3.2.1)

gsBoundCP

Conditional Power at Interim Boundaries
gsBound

Boundary derivation - low level
sfTruncated

Truncated, trimmed and gapped spending functions
hGraph

Create multiplicity graphs using ggplot2
plot.gsDesign

Plots for group sequential designs
sfTDist

t-distribution Spending Function
normalGrid

Normal Density Grid
gsDensity

Group sequential design interim density function
gsDesign

Design Derivation
gsDesign package overview

1.0 Group Sequential Design
gsCP

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

Normal distribution sample size (2-sample)
summary.gsDesign

Bound Summary and Z-transformations
condPower

Sample size re-estimation based on conditional power
Spending_Function_Overview

4.0: Spending function overview
gsProbability

Boundary Crossing Probabilities
sfExponential

Exponential Spending Function
sfHSD

Hwang-Shih-DeCani Spending Function
eEvents

Expected number of events for a time-to-event study
sfPower

Kim-DeMets (power) Spending Function
sfPoints

Pointwise Spending Function
print.nSurv

Advanced time-to-event sample size calculation
ciBinomial

Testing, Confidence Intervals, Sample Size and Power for Comparing Two Binomial Rates
sequentialPValue

Sequential p-value computation
checkLengths

Utility functions to verify variable properties
gsBinomialExact

One-Sample Binomial Routines
print.nSurvival

Time-to-event sample size calculation (Lachin-Foulkes)
sfLDOF

Lan-DeMets Spending function overview
xtable

xtable
sfLogistic

Two-parameter Spending Function Families
sfLinear

Piecewise Linear and Step Function Spending Functions