Package index • lrstat

All functions

BOINTable(): BOIN Decision Table for Dose-Finding Trials
ClopperPearsonCI(): Clopper-Pearson Confidence Interval for One-Sample Proportion
accrual(): Number of Enrolled Subjects
adaptDesign(): Adaptive Design at an Interim Look
aml: Acute myelogenous leukemia survival data from the survival package
assess_phregr(): Assess Proportional Hazards Assumption Based on Supremum Test
binary_tte_sim(): Simulation for a Binary and a Time-to-Event Endpoint in Group Sequential Trials
caltime(): Calendar Times for Target Number of Events
corr_pfs_os(): Correlation Between PFS and OS Given Correlation Between PD and OS
covrmst(): Covariance Between Restricted Mean Survival Times
dtpwexp(): Density Function of Truncated Piecewise Exponential Distribution
errorSpent(): Error Spending
exitprob(): Stagewise Exit Probabilities
exitprob_tsssd(): Exit Probabilities for Two-Stage Seamless Sequential Design (TSSSD)
fadjpbon(): Adjusted p-Values for Bonferroni-Based Graphical Approaches
fadjpdun(): Adjusted p-Values for Dunnett-Based Graphical Approaches
fadjpsim(): Adjusted p-Values for Simes-Based Graphical Approaches
float_to_fraction(): Converting a decimal to a fraction
fmodmix(): Adjusted p-Values for Modified Mixture Gatekeeping Procedures
fquantile(): The Quantiles of a Survival Distribution
fseqbon(): Group Sequential Trials Using Bonferroni-Based Graphical Approaches
fstdmix(): Adjusted p-Values for Standard Mixture Gatekeeping Procedures
fstp2seq(): Adjusted p-Values for Stepwise Testing Procedures for Two Sequences
ftrunc(): Adjusted p-Values for Holm, Hochberg, and Hommel Procedures
fwgtmat(): Weight Matrix for All Intersection Hypotheses
getADCI(): Confidence Interval After Adaptation
getADRCI(): Repeated Confidence Interval After Adaptation
getAccrualDurationFromN(): Accrual Duration to Enroll Target Number of Subjects
getBound(): Efficacy Boundaries for Group Sequential Design
getBound_tsssd(): Efficacy Boundaries for Two-Stage Seamless Sequential Design (TSSSD)
getCI(): Confidence Interval After Trial Termination
getCP(): Conditional Power Allowing for Varying Parameter Values
getDesign(): Power and Sample Size for a Generic Group Sequential Design
getDesignANOVA(): Power and Sample Size for One-Way ANOVA
getDesignANOVAContrast(): Power and Sample Size for One-Way ANOVA Contrast
getDesignAgreement(): Power and Sample Size for Cohen's kappa
getDesignEquiv(): Power and Sample Size for a Generic Group Sequential Equivalence Design
getDesignFisherExact(): Power and Sample Size for Fisher's Exact Test for Two Proportions
getDesignLogistic(): Power and Sample Size for Logistic Regression
getDesignMeanDiff(): Group Sequential Design for Two-Sample Mean Difference
getDesignMeanDiffCarryover(): Power and Sample Size for Direct Treatment Effects in Crossover Trials
getDesignMeanDiffCarryoverEquiv(): Power and Sample Size for Equivalence in Direct Treatment Effects in Crossover Trials
getDesignMeanDiffEquiv(): Group Sequential Design for Equivalence in Two-Sample Mean Difference
getDesignMeanDiffMMRM(): Group Sequential Design for Two-Sample Mean Difference From the MMRM Model
getDesignMeanDiffXO(): Group Sequential Design for Mean Difference in 2x2 Crossover
getDesignMeanDiffXOEquiv(): Group Sequential Design for Equivalence in Mean Difference in 2x2 Crossover
getDesignMeanRatio(): Group Sequential Design for Two-Sample Mean Ratio
getDesignMeanRatioEquiv(): Group Sequential Design for Equivalence in Two-Sample Mean Ratio
getDesignMeanRatioXO(): Group Sequential Design for Mean Ratio in 2x2 Crossover
getDesignMeanRatioXOEquiv(): Group Sequential Design for Equivalence in Mean Ratio in 2x2 Crossover
getDesignOddsRatio(): Group Sequential Design for Two-Sample Odds Ratio
getDesignOddsRatioEquiv(): Group Sequential Design for Equivalence in Two-Sample Odds Ratio
getDesignOneMean(): Group Sequential Design for One-Sample Mean
getDesignOneMultinom(): Power and Sample Size for One-Sample Multinomial Response
getDesignOneProportion(): Group Sequential Design for One-Sample Proportion
getDesignOneRateExact(): Power and Sample Size for One-Sample Poisson Rate Exact Test
getDesignOneSlope(): Group Sequential Design for One-Sample Slope
getDesignOrderedBinom(): Power and Sample Size for Cochran-Armitage Trend Test for Ordered Multi-Sample Binomial Response
getDesignPairedMeanDiff(): Group Sequential Design for Paired Mean Difference
getDesignPairedMeanDiffEquiv(): Group Sequential Design for Equivalence in Paired Mean Difference
getDesignPairedMeanRatio(): Group Sequential Design for Paired Mean Ratio
getDesignPairedMeanRatioEquiv(): Group Sequential Design for Equivalence in Paired Mean Ratio
getDesignPairedPropMcNemar(): Group Sequential Design for McNemar's Test for Paired Proportions
getDesignRepeatedANOVA(): Power and Sample Size for Repeated-Measures ANOVA
getDesignRepeatedANOVAContrast(): Power and Sample Size for One-Way Repeated Measures ANOVA Contrast
getDesignRiskDiff(): Group Sequential Design for Two-Sample Risk Difference
getDesignRiskDiffEquiv(): Group Sequential Design for Equivalence in Two-Sample Risk Difference
getDesignRiskDiffExact(): Power and Sample Size for Exact Unconditional Test for Risk Difference
getDesignRiskDiffExactEquiv(): Power and Sample Size for Exact Unconditional Test for Equivalence in Risk Difference
getDesignRiskRatio(): Group Sequential Design for Two-Sample Risk Ratio
getDesignRiskRatioEquiv(): Group Sequential Design for Equivalence in Two-Sample Risk Ratio
getDesignRiskRatioExact(): Power and Sample Size for Exact Unconditional Test for Risk Ratio
getDesignRiskRatioExactEquiv(): Power and Sample Size for Exact Unconditional Test for Equivalence in Risk Ratio
getDesignRiskRatioFM(): Group Sequential Design for Two-Sample Risk Ratio Based on the Farrington-Manning Score Test
getDesignSlopeDiff(): Group Sequential Design for Two-Sample Slope Difference
getDesignSlopeDiffMMRM(): Group Sequential Design for Two-Sample Slope Difference From the MMRM Model
getDesignTwoMultinom(): Power and Sample Size for Difference in Two-Sample Multinomial Responses
getDesignTwoOrdinal(): Power and Sample Size for the Wilcoxon Test for Two-Sample Ordinal Response
getDesignTwoWayANOVA(): Power and Sample Size for Two-Way ANOVA
getDesignUnorderedBinom(): Power and Sample Size for Unordered Multi-Sample Binomial Response
getDesignUnorderedMultinom(): Power and Sample Size for Unordered Multi-Sample Multinomial Response
getDesignWilcoxon(): Group Sequential Design for Two-Sample Wilcoxon Test
getDesign_tsssd(): Power and Sample Size for Two-Stage Seamless Sequential Design (TSSSD)
getDurationFromNevents(): Range of Accrual Duration for Target Number of Events
getNeventsFromHazardRatio(): Required Number of Events Given Hazard Ratio
getRCI(): Repeated Confidence Interval for Group Sequential Design
hazard_pd(): Hazard Function for Progressive Disease (PD) Given Correlation Between PD and OS
hazard_sub(): Hazard Function for Sub Population
heart: Stanford heart transplant data from the survival package
hedgesg(): Hedges' g Effect Size
immdef: Simulated CONCORDE trial data from the rpsftm package
ingots: The binary data from Cox and Snell (1989, pp. 10-11).
kmdiff(): Estimate of Milestone Survival Difference
kmest(): Kaplan-Meier Estimates of Survival Curve
kmpower(): Power for Difference in Milestone Survival Probabilities
kmpower1s(): Power for One-Sample Milestone Survival Probability
kmpowerequiv(): Power for Equivalence in Milestone Survival Probability Difference
kmsamplesize(): Sample Size for Difference in Milestone Survival Probabilities
kmsamplesize1s(): Sample Size for One-Sample Milestone Survival Probability
kmsamplesizeequiv(): Sample Size for Equivalence in Milestone Survival Probability Difference
kmstat(): Stratified Difference in Milestone Survival Probabilities
liferegr(): Parametric Regression Models for Failure Time Data
liver: The liver data used in SAS PROC PHREG documentation examples.
logisregr(): Logistic Regression Models for Binary Data
lrpower(): Log-Rank Test Power
lrpowerequiv(): Power for Equivalence in Hazard Ratio
lrsamplesize(): Log-Rank Test Sample Size
lrsamplesizeequiv(): Sample Size for Equivalence in Hazard Ratio
lrschoenfeld(): Schoenfeld Method for Log-Rank Test Sample Size Calculation
lrsim(): Log-Rank Test Simulation
lrsim2e(): Log-Rank Test Simulation for PFS and OS Endpoints
lrsim2e3a(): Log-Rank Test Simulation for Two Endpoints (PFS and OS) and Three Arms
lrsim3a(): Log-Rank Test Simulation for Three Arms
lrsim_tsssd(): Log-Rank Test Simulation for Multiple Active Arms vs. Common Control
lrsimsub(): Log-Rank Test Simulation for Enrichment Design
lrstat-package: Power and Sample Size Calculation for Non-Proportional Hazards and Beyond
lrstat(): Number of Subjects Having an Event and Log-Rank Statistics
lrtest(): Log-Rank Test of Survival Curve Difference
mTPI2Table(): mTPI-2 Decision Table
mnOddsRatioCI(): Miettinen-Nurminen Score Confidence Interval for Two-Sample Odds Ratio
mnRateDiffCI(): Miettinen-Nurminen Score Confidence Interval for Two-Sample Rate Difference
mnRateRatioCI(): Miettinen-Nurminen Score Confidence Interval for Two-Sample Rate Ratio
mnRiskDiffCI(): Miettinen-Nurminen Score Confidence Interval for Two-Sample Risk Difference
mnRiskRatioCI(): Miettinen-Nurminen Score Confidence Interval for Two-Sample Risk Ratio
mtpwexp(): Mean and Variance of Truncated Piecewise Exponential Distribution
natrisk(): Number of Subjects at Risk
nbpower(): Power for Negative Binomial Rate Ratio
nbpower1s(): Power for One-Sample Negative Binomial Rate
nbpowerequiv(): Power for Equivalence in Negative Binomial Rate Ratio
nbsamplesize(): Sample Size for Negative Binomial Rate Ratio
nbsamplesize1s(): Sample Size for One-Sample Negative Binomial Rate
nbsamplesizeequiv(): Sample Size for Equivalence in Negative Binomial Rate Ratio
nbstat(): Negative Binomial Rate Ratio
nevent(): Number of Subjects Having an Event by Calendar Time
patrisk(): Probability of Being at Risk
pbvnorm(): Distribution Function of the Standard Bivariate Normal
pevent(): Probability of Having an Event
phregr(): Proportional Hazards Regression Models
pmvnormr(): Multivariate Normal Probabilities over a Hyperrectangle
ptpwexp(): Distribution Function of Truncated Piecewise Exponential Distribution
pwexpcuts(): Piecewise Exponential Approximation to a Survival Distribution
pwexploglik(): Profile Log-Likelihood Function for Change Points in Piecewise Exponential Approximation
qmvnormr(): Equicoordinate Quantiles of the Multivariate Normal Distribution
qtpwexp(): Quantile Function of Truncated Piecewise Exponential Distribution
rawdata: A simulated time-to-event data set with 10 replications
remlOddsRatio(): REML Estimates of Individual Proportions With Specified Odds Ratio
remlRateDiff(): REML Estimates of Individual Rates With Specified Rate Difference
remlRateRatio(): REML Estimates of Individual Rates With Specified Rate Ratio
remlRiskDiff(): REML Estimates of Individual Proportions With Specified Risk difference
remlRiskRatio(): REML Estimates of Individual Proportions With Specified Risk Ratio
repeatedPValue(): Repeated p-Values for Group Sequential Design
residuals_liferegr(): Residuals for Parametric Regression Models for Failure Time Data
residuals_phregr(): Residuals for Proportional Hazards Regression Models
riskDiffExactCI(): Exact Unconditional Confidence Interval for Risk Difference
riskDiffExactPValue(): P-Value for Exact Unconditional Test of Risk Difference
riskRatioExactCI(): Exact Unconditional Confidence Interval for Risk Ratio
riskRatioExactPValue(): P-Value for Exact Unconditional Test of Risk Ratio
rmdiff(): Estimate of Restricted Mean Survival Time Difference
rmest(): Estimate of Restricted Mean Survival Time
rmpower(): Power for Difference in Restricted Mean Survival Times
rmpower1s(): Power for One-Sample Restricted Mean Survival Time
rmpowerequiv(): Power for Equivalence in Restricted Mean Survival Time Difference
rmsamplesize(): Sample Size for Difference in Restricted Mean Survival Times
rmsamplesize1s(): Sample Size for One-Sample Restricted Mean Survival Time
rmsamplesizeequiv(): Sample Size for Equivalence in Restricted Mean Survival Time Difference
rmst(): Restricted Mean Survival Time
rmstat(): Stratified Difference in Restricted Mean Survival Times
rtpwexp(): Random Number Generation Function of Truncated Piecewise Exponential Distribution
runShinyApp_lrstat(): Run Shiny App
sexagg: Urinary tract infection data from the logistf package
shilong: The randomized clinical trial SHIVA data in long format from the ipcwswitch package
simon2stage(): Simon's Two-Stage Design
simonBayesAnalysis(): Analysis of Simon's Bayesian Basket Trials
simonBayesSim(): Simulation of Simon's Bayesian Basket Trials
six: The repeated measures data from the "Six Cities" study of the health effects of air pollution (Ware et al. 1984).
survQuantile(): Brookmeyer-Crowley Confidence Interval for Quantiles of Right-Censored Time-to-Event Data
survfit_phregr(): Survival Curve for Proportional Hazards Regression Models
svdcpp(): Singular Value Decomposition of a Matrix
tobin: Tobin's tobit data from the survival package
updateGraph(): Update Graph for Graphical Approaches
zph_phregr(): Assess Proportional Hazards Assumption Based on Scaled Schoenfeld Residuals
zstatOddsRatio(): Miettinen-Nurminen Score Test Statistic for Two-Sample Odds Ratio
zstatRateDiff(): Miettinen-Nurminen Score Test Statistic for Two-Sample Rate Difference
zstatRateRatio(): Miettinen-Nurminen Score Test Statistic for Two-Sample Rate Ratio
zstatRiskDiff(): Miettinen-Nurminen Score Test Statistic for Two-Sample Risk difference
zstatRiskRatio(): Miettinen-Nurminen Score Test Statistic for Two-Sample Risk Ratio