Package: NCC 1.0

Pavla Krotka

NCC: Simulation and Analysis of Platform Trials with Non-Concurrent Controls

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

Authors:Pavla Krotka [aut, cre], Marta Bofill Roig [aut, ths], Katharina Hees [aut], Peter Jacko [aut], Dominic Magirr [aut], Martin Posch [ctb]

NCC_1.0.tar.gz
NCC_1.0.tar.gz(r-4.7-any)NCC_1.0.tar.gz(r-4.6-any)
NCC_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
NCC/json (API)

# Install 'NCC' in R:
install.packages('NCC', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pavlakrotka/ncc/issues

Pkgdown/docs site:https://pavlakrotka.github.io

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

3.70 score 25 scripts 218 downloads 34 exports 96 dependencies

Last updated from:5af99152c8. Checks:2 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE266
source / vignettesOK220
linux-release-x86_64NOTE258
wasm-releaseOK162

Exports:all_modelsdatasim_bindatasim_contfixmodel_binfixmodel_cal_binfixmodel_cal_contfixmodel_contgam_contget_ss_matrixinv_u_trendlinear_trendMAPprior_binMAPprior_contmixmodel_AR1_cal_contmixmodel_AR1_contmixmodel_cal_contmixmodel_contpiecewise_cal_contpiecewise_contplot_trialpoolmodel_binpoolmodel_contseasonal_trendsepmodel_adj_binsepmodel_adj_contsepmodel_binsepmodel_contsim_studysim_study_parsplines_cal_contsplines_contsw_trendtimemachine_bintimemachine_cont

Dependencies:abindassertthatbackportsbayesplotBHbootcallrcheckmateclicodacodetoolscpp11curldescdistributionaldoParalleldplyrfarverforeachFormulagenericsgeometryggplot2ggridgesgluegmpgridExtragtableinlineisobanditeratorsjsonlitelabelinglatticelifecyclelinproglme4lmerTestloolpSolvemagicmagickmagrittrMASSMatrixmatrixStatsmgcvminqamvtnormnlmenloptrnumDerivparallellypbapplypillarpkgbuildpkgconfigplyrposteriorprocessxproxypspurrrQuickJSRR6RBesTrbibutilsRColorBrewerRcppRcppEigenRcppParallelRcppProgressRdpackreformulasregistryreshape2rjagsrlangROIrstanrstantoolsS7scalesslamspaMMStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

How to run a simulation study

Rendered fromhow_to_run_sim_study.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2023-03-03
Started: 2023-03-03

How to simulate binary data

Rendered fromdatasim_bin.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2023-03-03
Started: 2023-03-03

How to simulate continuous data

Rendered fromdatasim_cont.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2023-03-03
Started: 2023-03-03

NCC Introduction

Rendered fromncc_intro.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2023-03-03
Started: 2023-03-03

Readme and manuals

Help Manual

Help pageTopics
Simulate binary data from a platform trial with a shared control arm and a given number of experimental treatment arms entering at given time pointsdatasim_bin
Simulate continuous data from a platform trial with a shared control arm and a given number of experimental treatment arms entering at given time pointsdatasim_cont
Frequentist logistic regression model analysis for binary data adjusting for periodsfixmodel_bin
Frequentist logistic regression model analysis for binary data adjusting for calendar time unitsfixmodel_cal_bin
Frequentist linear regression model analysis for continuous data adjusting for calendar time unitsfixmodel_cal_cont
Frequentist linear regression model analysis for continuous data adjusting for periodsfixmodel_cont
Generalized additive model analysis for continuous datagam_cont
Sample size matrix for a platform trial with a given number of treatment armsget_ss_matrix
Generation of an inverted-u trendinv_u_trend
Generation of a linear trend that starts in a given periodlinear_trend
Analysis for binary data using the MAP Prior approachMAPprior_bin
Analysis for continuous data using the MAP Prior approachMAPprior_cont
Mixed regression model analysis for continuous data adjusting for calendar time units as a random factor with AR1 correlation structuremixmodel_AR1_cal_cont
Mixed regression model analysis for continuous data adjusting for periods as a random factor with AR1 correlation structuremixmodel_AR1_cont
Mixed regression model analysis for continuous data adjusting for calendar time units as a random factormixmodel_cal_cont
Mixed regression model analysis for continuous data adjusting for periods as a random factormixmodel_cont
Model-based analysis for continuous data using discontinuous piecewise polynomials per calendar time unitpiecewise_cal_cont
Model-based analysis for continuous data using discontinuous piecewise polynomials per periodpiecewise_cont
Function for visualizing the simulated trialplot_trial
Pooled analysis for binary datapoolmodel_bin
Pooled analysis for continuous datapoolmodel_cont
Generation of a seasonal trendseasonal_trend
Separate analysis for binary data adjusted for periodssepmodel_adj_bin
Separate analysis for continuous data adjusted for periodssepmodel_adj_cont
Separate analysis for binary datasepmodel_bin
Separate analysis for continuous datasepmodel_cont
Wrapper function performing simulation studies for a given set of scenarios (not parallelized)sim_study
Wrapper function performing simulation studies for a given set of scenarios (parallelized on replication level)sim_study_par
Spline regression analysis for continuous data with knots placed according to calendar time unitssplines_cal_cont
Spline regression analysis for continuous data with knots placed according to periodssplines_cont
Generation of stepwise trend with equal jumps between periodssw_trend
Time machine analysis for binary datatimemachine_bin
Time machine analysis for continuous datatimemachine_cont