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gsearly (version 1.0.0)

Creates Group Sequential Trial Designs when Early Outcomes are Available

Description

Methods to construct and power group sequential clinical trial designs for outcomes at multiple times. Outcomes at earlier times provide information on the final (primary) outcome. A range of recruitment and correlation models are available as are methods to simulate data in order to explore design operating characteristics. For more details see Parsons (2024) .

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Version

Install

install.packages('gsearly')

Version

1.0.0

License

GPL (>= 2)

Maintainer

Nick Parsons

Last Published

January 23rd, 2026

Functions in gsearly (1.0.0)

plotPower

A power plot for a gsearly model
roundInterims

Round a gsearly design interim sample size to integer values
plot.gsearly

Plot a gsearly model
modelParameters

Estimates model parameters from raw data
qol

Quality of life index for patients with knee pain
tfuStandard

Standardize follow-up times
summary.gsearly

Summarise a gsearly model
simdataExtract

Extract simulated data for a single trial
fixedSampsize

Fixed model sample size for a gsearly model
expectSampsize

Expected sample size for a gsearly model
gsearly-package

Group sequential designs with early outcomes
dataOrder

Order a data frame by subject and ordered time variable
gsearlySimulate

Simulates data for a previously fitted gsearly model
corrExp

Exponential correlation matrix
corrUnif

Uniform correlation matrix
gsearlyUser

User input estimates of sample size and power for group sequential designs with early outcomes
gsearlyFit

Fit a generalized least squares model
gsearlyModel

Model based estimates of sample size and power for group sequential designs with early outcomes
plotRecruit

A recruitment plot for a gsearly model
plotBoundary

A boundary plot for a gsearly model
print.summary.gsearly

Print a gsearly model summary
print.gsearly

Print a gsearly model
plotInform

An information plot for a gsearly model