Package: eselect 1.1

eselect: Adaptive Clinical Trial Designs with Endpoint Selection and Sample Size Reassessment

Endpoint selection and sample size reassessment for multiple binary endpoints based on blinded and/or unblinded data. Trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation between endpoints. The implemented design is proposed in Bofill Roig, M., Gómez Melis, G., Posch, M., and Koenig, F. (2022). <doi:10.48550/arXiv.2206.09639>.

Authors:Marta Bofill Roig [aut, cre], Guadalupe Gomez Melis [ctb], Franz Koenig [ctb], Martin Posch [ctb]

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15 exports 0.00 score 76 dependencies 204 downloads

Last updated 2 years agofrom:2ab7a8e054. Checks:OK: 7. Indexed: yes.

Exports:corr_rest_bcorr_rest_ubeselecteselect_ubeselectsimeselectsim_ubestimation_bestimation_ubf_ORf_simfun_p0OR_functionsamplesize_ORtest_ftest_me

Dependencies:abindADGofTestbackportsbootbroomcarcarDataclicolorspaceCompAREdesigncopulacorrplotcowplotcpp11DerivdoBydplyrfansifarvergenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragslgtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpcaPPpillarpkgconfigpolynompsplinepurrrquantregR6RColorBrewerRcppRcppEigenrlangrootSolverstatixscalesSparseMstablediststringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr