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BuyseTest (version 3.3.4)

Generalized Pairwise Comparisons

Description

Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) for complete observations, and extended in Peron (2018) to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 ), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.

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Version

Install

install.packages('BuyseTest')

Monthly Downloads

1,117

Version

3.3.4

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/bozenne/BuyseTest

Maintainer

Brice Ozenne

Last Published

September 24th, 2025

Functions in BuyseTest (3.3.4)

BuyseTest-package

BuyseTest package: Generalized Pairwise Comparisons

BuyseTest.options-methods

Methods for the class "BuyseTest.options"

Multi-group GPC (EXPERIMENTAL)

Adjustment for Multiple Comparisons

C++ function performing the pairwise comparison over several endpoints.

BuyseTest.options-class

Class "BuyseTest.options" (global setting for the BuyseTest package)

BuyseTest.options

Global options for BuyseTest package

Time to Event Model

S4BuyseTest-class

Class "S4BuyseTest" (output of BuyseTest)

S4BuysePower-summary

Summary Method for Class "S4BuysePower"

S4BuysePower-print

Print Method for Class "S4BuysePower"

S4BuyseTest-confint

Extract Confidence Interval from GPC

S4BuyseTest-nobs

Sample Size for Class "S4BuyseTest"

S4BuyseTest-model.tables

Extract Summary for Class "S4BuyseTest"

S4BuysePower-nobs

Sample Size for Class "S4BuysePower"

S4BuyseTest-plot

Graphical Display for GPC

S4BuysePower-model.tables

Extract Summary for Class "S4BuysePower"

S4BuyseTest-coef

Extract Summary Statistics from GPC

Estimation of the Brier Score (EXPERIMENTAL)

S4BuyseTest-summary

Summary Method for Class "S4BuyseTest"

S4BuyseTest-print

Print Method for Class "S4BuyseTest"

S4BuysePower-show

Show Method for Class "S4BuysePower"

S4BuyseTest-update

Re-run two-group GPC

.calcIntegralCif_cpp

C++ Function Computing the Integral Terms for the Peron Method in the presence of competing risks (CR).

coef.BuyseTestBrier

Extract the Brier Score

coef.BuyseTestAuc

Extract the AUC Value

N-of-1 trials with On-demand Sildenafil as a Treatment for Raynaud Phenomenon

RCT In Chronic Heart Failure Assessing an Inhibitor of the Renin-Angiotensin System.

calcIntegralSurv2_cpp

C++ Function pre-computing the Integral Terms for the Peron Method in the survival case.

.calcIntegralSurv_cpp

C++ Function Computing the Integral Terms for the Peron Method in the survival case.

Strata creation

confint.BuyseTestBrier

Extract the Brier Score with its Confidence Interval

confint.BuyseTestAuc

Extract the AUC value with its Confidence Interval

Estimation of the Area Under the ROC Curve (EXPERIMENTAL)

autoplot.S4BuyseTest

Graphical Display for GPC

.rowCumProd_cpp

Apply cumprod in each row

.rowMultiply_cpp

Multiply by a vector of values in each row

Row-wise cumulative sum

Constrained Kaplan-Meier Estimator

Substract a vector of values in each column

Extract the Number of Favorable, Unfavorable, Neutral, Uninformative pairs

Extract the pseudovalues of the Estimator

Substract a vector of values in each row

S4BuyseTest-vcov

Extract Uncertainty from GPC

Column-wise cumulative sum

Rare disease trial

RCT In Metastatic Pancreatic Cancer Comparing Two Chemoterapy.

Divide by a vector of values in each column

Extract the Survival and Survival Jumps

as.data.table.performance

Convert Performance Objet to data.table

Extract i.i.d. decomposition from a prodlim model

Extract the H-decomposition of the Estimator

.colMultiply_cpp

Multiply by a vector of values in each column

Inference using MOVER in match designs (EXPERIMENTAL)

iid.BuyseTestAuc

Extract the idd Decomposition for the AUC

Extract the Score of Each Pair

Dividy by a vector of values in each row

iid.BuyseTestBrier

Extract the idd Decomposition for the Brier Score

Assess Performance of a Classifier

summary.performance

Summary Method for Performance Objects

plot.S3sensitivity

Graphical Display for Sensitivity Analysis

simCompetingRisks

Simulation of Gompertz competing risks data for the BuyseTest

predict.BuyseTTEM

Prediction with Time to Event Model

performanceResample

Uncertainty About Performance of a Classifier (EXPERIMENTAL)

Sensitivity Analysis for the Choice of the Thresholds

Simulation of data for the BuyseTest

rbind.performance

Combine Resampling Results For Performance Objects

Check Arguments of a function.

Performing simulation studies with BuyseTest

S4BuysePower-class

Class "S4BuysePower" (output of BuyseTest)