Generalized Pairwise Comparisons
Extract the idd Decomposition for the Brier Score
Simulation of data for the BuyseTest
Simulation of Gompertz competing risks data for the BuyseTest
Summary Method for Performance Objects
Convert Performance Objet to data.table
Estimation of the Area Under the ROC Curve (EXPERIMENTAL)
Graphical Display for GPC
Estimation of the Brier Score (EXPERIMENTAL)
Adjustment for Multiple Comparisons
BuyseTest package: Generalized Pairwise Comparisons
Class "BuyseTest.options" (global setting for the BuyseTest package)
Methods for the class "BuyseTest.options"
Global options for BuyseTest package
Two-group GPC
Time to Event Model
C++ Function pre-computing the Integral Terms for the Peron Method in ...
Multi-group GPC (EXPERIMENTAL)
Extract the AUC Value
Extract the Brier Score
Extract the AUC value with its Confidence Interval
Extract the Brier Score with its Confidence Interval
Strata creation
C++ Function Computing the Integral Terms for the Peron Method in the ...
C++ Function Computing the Integral Terms for the Peron Method in the ...
Substract a vector of values in each column
Column-wise cumulative sum
Multiply by a vector of values in each column
Divide by a vector of values in each column
Substract a vector of values in each row
Apply cumprod in each row
Row-wise cumulative sum
Multiply by a vector of values in each row
Dividy by a vector of values in each row
C++ function performing the pairwise comparison over several endpoints...
Extract the idd Decomposition for the AUC
Extract i.i.d. decomposition from a prodlim model
Assess Performance of a Classifier
Uncertainty About Performance of a Classifier (EXPERIMENTAL)
Graphical Display for Sensitivity Analysis
Performing simulation studies with BuyseTest
Prediction with Time to Event Model
Combine Resampling Results For Performance Objects
Class "S4BuysePower" (output of BuyseTest)
Extract Summary for Class "S4BuysePower"
Sample Size for Class "S4BuysePower"
Print Method for Class "S4BuysePower"
Summary Method for Class "S4BuysePower"
Class "S4BuyseTest" (output of BuyseTest)
Extract Summary Statistics from GPC
Extract Confidence Interval from GPC
Extract the Number of Favorable, Unfavorable, Neutral, Uninformative p...
Extract the H-decomposition of the Estimator
Extract the Score of Each Pair
Extract the pseudovalues of the Estimator
Extract the Survival and Survival Jumps
Extract Summary for Class "S4BuyseTest"
Sample Size for Class "S4BuyseTest"
Graphical Display for GPC
Print Method for Class "S4BuyseTest"
Sensitivity Analysis for the Choice of the Thresholds
Show Method for Class "S4BuysePower"
Summary Method for Class "S4BuyseTest"
Check Arguments of a function.
Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> 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 than a random observation drawn from the other group (Mann-Whitney parameter). The net benefit and win ratio statistics, i.e. the difference and ratio between the probabilities relative to the intervention and control groups, can then also be estimated. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), 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.