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

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

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
binomialPowerTable(): Power Table for Binomial Tests

Early, independent and more limited implementation of Lachin and Foulkes methods are in nSurvival() and print.nSurvival().

tEventsIA() nEventsIA() nSurv() print(<nSurv>) xtable(<gsSurv>) gsSurv() print(<gsSurv>): Advanced time-to-event sample size calculation
gsSurvCalendar(): Group sequential design with calendar-based timing of analyses
gsSurvPower(): Compute power for a group sequential survival design
print(<nSurvival>) nSurvival() nEvents() zn2hr() hrn2z() hrz2n(): Time-to-event sample size calculation (Lachin-Foulkes)
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)
medianFollowUp() minMedianFollowUp(): Median follow-up across all planned participants
plotMinMedianFollowUp(): Plot median follow-up across all planned participants

toBinomialExact(): Translate survival design bounds to exact binomial bounds
VEtable(): Summarize an exact binomial vaccine or prevention efficacy design
lt(<gsVETable>): Format a vaccine or prevention efficacy summary table
ciBinomialExact(): Exact confidence intervals for vaccine or prevention efficacy
repeatedCIBinomialExact(): Exact repeated confidence intervals for vaccine or prevention efficacy
sequentialCIBinomialExact(): Exact sequential confidence intervals for vaccine or prevention efficacy
gsCPBinomialExact(): Exact conditional power for a group sequential binomial design
repeatedPValueBinomialExact(): Exact binomial repeated p-values for a group sequential design
sequentialPValueBinomialExact(): Exact binomial sequential p-value for a group sequential design
simBinomialSeasonalExact(): Simulate exact-binomial seasonal monitoring scenarios

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
gsCPFutilitySpending(): Calibrate Futility Spending to Conditional Power Targets
gsPPFutilitySpending(): Calibrate Futility Spending to Predictive Power Targets
gsCPOSFutilitySpending(): Calibrate Futility Spending to Conditional Probability of Success
gsCAFutilitySpending(): Calibrate Futility Spending at Fixed Information
gsPOSFutilitySpending(): Calibrate Futility Spending to Unconditional Probability of Success
gsEffectSpending(): Calibrate Spending to Natural-Scale Effects at Boundaries

gsCP() gsPP() gsPI() gsPosterior() gsPOS() gsCPOS(): Conditional and Predictive Power, Overall and Conditional Probability of Success
gsCPFutilitySpending(): Calibrate Futility Spending to Conditional Power Targets
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

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

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

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