Generalized Pairwise Comparisons

Implementation of the Generalized Pairwise Comparisons (GPC). GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints. The net benefit and win ratio statistics can then be estimated and corresponding confidence intervals and p-values can be estimated using resampling methods or the asymptotic U-statistic theory. The software enables the use of thresholds of minimal importance difference, stratification, and corrections to deal with right-censored endpoints or missing values.


-- mode: org --

  • Version 1.7 ** User visble changes
  • NEW FEATURE: iid function

** Internal change

  • fix bug in the computation of the asymptotic variance + add tests
  • Version 1.6 ** Internal change

    • simplify C++ code.
  • Version 1.5 ** User visble changes

    • Argument statistic now takes values netBenefit or winRatio (instead of netChance or winRatio).
    • NEW FEATURE: standard errors/p.values/confidence intervals can now be computed without resampling setting the option method.tte to asymptotic
    • NEW FEATURE: a function powerBuyseTest has been added to perform simulation studies with BuyseTest (e.g. compute power, coverage or bias).

** Internal change

  • improve the management of the weights of the pairs from one endpoint to another.
  • Version 1.4 ** User visble changes - BuyseTest function
    • NEW FEATURE: Instead of estimating the survival curves on the data used for the GPC, BuyseTest can use pre-defined survival models for method "Peron" (argument model.tte).
    • NEW FEATURE: methods getSurvival to access the survival probability used by BuyseTest.
    • NEW FEATURE: getPairScore to access the score of each pair.
    • NEW FEATURE: argument method.uninf in BuyseTest enables to re-attribute the uninformative scores to favorable/unfavorable/neutral.

** Internal change

  • improve initSurvival and the computation of the Peron (C++ code)
  • change the handling of NA in the survival when using method.tte="Peron" (C++ code)
  • change the way neutralAsUninf is implemented (C++ code)
  • Version 1.3 ** User visble changes - BuyseTest function
    • NEW FEATURE: boostrap resampling (method.inference = "boostrap")
    • NEW FEATURE: confint method to extract confidence intervals
    • NEW FEATURE: BuyseTest can handle competing risks (experimental)
    • argument n.permutation becomes n.resampling
    • argument method becomes method.tte
    • add argument method.inference to choose how to compute pvalues and CI.
    • option method.tte="Peto" and method.tte="Efron" have been removed.

** Internal change

  • reorganize BuyseTest into BuyseTest and .BuyseTest. Make initalization and testing of the arguments independent
  • remove dependency on tcltk.
  • confidence intervals and p.values are computed outside BuyseTest, when calling summary.
  • Version 1.2 ** User visble changes - BuyseTest function
    • add argument keepComparison, operator.
    • argument n.bootstrap becomes n.permuation
    • argument neutralAsUninf becomes
    • nicer display with summary
    • lighter display when printing the object

** Internal change

  • reorganize the tests
  • Version 1.1
    • add a formula argument to the BuyseTest function. This can be used instead of the treatment, endpoint, threshold, type, censoring, and strata argument to specify the prioritized endpoints.
    • unify the C++ code, add a threshold for considering a pair non-informative (w>1e-12). May induce differences with previous versions in the index of uniformative pairs.
    • uses prodlim instead of survival to compute the KM estimates.
    • Add a neutralAsUnif argument to the BuyseTest function to decide whether the analysis should continue on lower priority when a pair is classified as neutral

Reference manual

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1.7 by Brice Ozenne, 9 months ago

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Browse source code at

Authors: Brice Ozenne [aut, cre] , Julien Peron [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports doParallel, foreach, methods, lava, parallel, stats, stats4, utils

Depends on prodlim, Rcpp, data.table

Suggests pbapply, R.rsp, survival, testthat

Linking to Rcpp, RcppArmadillo

System requirements: C++11

See at CRAN