Implementation of the Generalized Pairwise Comparisons.
BuyseTest is the main function of the package. See the vignette of an overview of the functionalities of the package.
Run citation("BuyseTest") in R for how to cite this package in scientific publications.
See the section reference below for examples of application in clinical studies.
The Generalized Pairwise Comparisons form all possible pairs of observations, one observation being taken from the intervention group and the other is taken from the control group, and compare the value of their endpoints.
If the difference in endpoint value between the two observations of the pair is greater than the threshold of clinical relevance, the pair is classified as favorable (i.e. win). If the difference is lower than minus the threshold of clinical relevance the pair is classified as unfavorable (i.e. loss). Otherwise the pair is classified as neutral. In presence of censoring, it might not be possible to compare the difference to the threshold. In such cases the pair is classified as uninformative.
Simultaneously analysis of several endpoints is performed by prioritizing the endpoints, assigning the highest priority to the endpoint considered the most clinically relevant. The endpoint with highest priority is analyzed first, and neutral and uninformative pair are analyzed regarding endpoint of lower priority.
Examples of application in clinical studies: J. Peron, P. Roy, K. Ding, W. R. Parulekar, L. Roche, M. Buyse (2015). Assessing the benefit-risk of new treatments using generalized pairwise comparisons: the case of erlotinib in pancreatic cancer. British journal of cancer 112:(6)971-976. J. Peron, P. Roy, T. Conroy, F. Desseigne, M. Ychou, S. Gourgou-Bourgade, T. Stanbury, L. Roche, B. Ozenne, M. Buyse (2016). An assessment of the benefit-risk balance of FOLFORINOX in metastatic pancreatic adenocarcinoma. Oncotarget 7:82953-60, 2016.
Comparison between the net benefit and alternative measures of treatment effect: J. Peron, P. Roy, B. Ozenne, L. Roche, M. Buyse (2016). The net chance of a longer survival as a patient-oriented measure of benefit in randomized clinical trials. JAMA Oncology 2:901-5. E. D. Saad , J. R. Zalcberg, J. Peron, E. Coart, T. Burzykowski, M. Buyse (2018). Understanding and communicating measures of treatment effect on survival: can we do better?. J Natl Cancer Inst.