Package index • gsDesign

Group Sequential Computation

For an overview of the gsDesign package, see vignette("gsDesignPackageOverview").

gsDesign() xtable(<gsDesign>): Design Derivation

plot(<gsDesign>) plot(<gsProbability>): Plots for group sequential designs

gsProbability() print(<gsProbability>): Boundary Crossing Probabilities

gsBound() gsBound1(): Boundary derivation - low level

sequentialPValue(): Sequential p-value computation

Design Characterization

summary(<gsDesign>) print(<gsDesign>) gsBoundSummary() xprint() print(<gsBoundSummary>) gsBValue() gsDelta() gsRR() gsHR() gsCPz(): Bound Summary and Z-transformations

xtable: xtable

Normal Endpoint Design

nNormal(): Normal distribution sample size (2-sample)

Binomial Endpoint Design

ciBinomial() nBinomial() simBinomial() testBinomial() varBinomial(): Testing, Confidence Intervals, Sample Size and Power for Comparing Two Binomial Rates

summary(<gsDesign>) print(<gsDesign>) gsBoundSummary() xprint() print(<gsBoundSummary>) gsBValue() gsDelta() gsRR() gsHR() gsCPz(): Bound Summary and Z-transformations

Time-to-Event Endpoint Design

print(<nSurvival>) nSurvival() nEvents() zn2hr() hrn2z() hrz2n(): Time-to-event sample size calculation (Lachin-Foulkes)

print(<nSurv>) nSurv() tEventsIA() nEventsIA() gsSurv() print(<gsSurv>) xtable(<gsSurv>): Advanced time-to-event sample size calculation

gsSurvCalendar(): Time-to-event endpoint design with calendar timing of analyses

summary(<gsDesign>) print(<gsDesign>) gsBoundSummary() xprint() print(<gsBoundSummary>) gsBValue() gsDelta() gsRR() gsHR() gsCPz(): Bound Summary and Z-transformations

eEvents() print(<eEvents>): Expected number of events for a time-to-event study

toInteger(): Translate group sequential design to integer events (survival designs) or sample size (other designs)

Vaccine/Prevention Efficacy

toBinomialExact(): Translate survival design bounds to exact binomial bounds

Spending Functions

For an overview of spending functions, see vignette("SpendingFunctionOverview").

summary(<spendfn>) spendingFunction(): Spending Function

sfLDOF() sfLDPocock(): Lan-DeMets Spending function overview

sfHSD(): Hwang-Shih-DeCani Spending Function

sfPower(): Kim-DeMets (power) Spending Function

sfExponential(): Exponential Spending Function

sfLogistic() sfBetaDist() sfCauchy() sfExtremeValue() sfExtremeValue2() sfNormal(): Two-parameter Spending Function Families

sfTDist(): t-distribution Spending Function

sfLinear() sfStep(): Piecewise Linear and Step Function Spending Functions

sfPoints(): Pointwise Spending Function

sfTruncated() sfTrimmed() sfGapped(): Truncated, trimmed and gapped spending functions

sfXG1() sfXG2() sfXG3(): Xi and Gallo conditional error spending functions

Conditional and Predictive Power

gsCP() gsPP() gsPI() gsPosterior() gsPOS() gsCPOS(): Conditional and Predictive Power, Overall and Conditional Probability of Success

summary(<gsDesign>) print(<gsDesign>) gsBoundSummary() xprint() print(<gsBoundSummary>) gsBValue() gsDelta() gsRR() gsHR() gsCPz(): Bound Summary and Z-transformations

gsBoundCP(): Conditional Power at Interim Boundaries

normalGrid(): Normal Density Grid

gsDensity(): Group sequential design interim density function

Sample Size Adaptation

condPower() ssrCP() plot(<ssrCP>) z2NC() z2Z() z2Fisher() Power.ssrCP(): Sample size re-estimation based on conditional power

Single Arm Binomial Design

gsBinomialExact() binomialSPRT() plot(<gsBinomialExact>) plot(<binomialSPRT>) nBinomial1Sample(): One-Sample Binomial Routines

Input Checking

checkLengths() checkRange() checkScalar() checkVector() isInteger(): Utility functions to verify variable properties

Testing multiple hypotheses

hGraph(): Create multiplicity graphs using ggplot2

Summary tables

as_table(): Create a summary table

as_gt(): Convert a summary table object to a gt object

as_rtf(): Save a summary table object as an RTF file