Package: NCC 1.0

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.zip(r-4.7)NCC_1.0.zip(r-4.6)NCC_1.0.zip(r-4.5)
NCC_1.0.tgz(r-4.6-any)NCC_1.0.tgz(r-4.5-any)
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✨
DESCRIPTION
card.svg |card.png
NCC/json (API)

# Install 'NCC' in R:
install.packages('NCC', repos = c('https://pavlakrotka.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
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

clinical-trialsplatform-trialssimulationstatistical-inferencejagscpp

5.90 score 5 stars 32 scripts 265 downloads 40 exports 103 dependencies

Last updated from:f439de7b78. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK279
source / vignettesOK249
linux-release-x86_64OK283
macos-release-arm64OK225
macos-oldrel-arm64OK230
windows-develOK233
windows-releaseOK235
windows-oldrelOK245
wasm-releaseOK149

Exports:all_modelsdatasim_bindatasim_bin_2datasim_contfixmodel_binfixmodel_cal_binfixmodel_cal_contfixmodel_contfixmodel_lin_contgam_contget_ss_matrixinv_u_trendlinear_trendMAPprior_binMAPprior_contMAPpriorNew_contmixmodel_AR1_cal_contmixmodel_AR1_contmixmodel_cal_contmixmodel_contmixmodel_int_cal_contmixmodel_int_contpiecewise_cal_contpiecewise_contplot_trialpoolmodel_binpoolmodel_contpowerprior_contseasonal_trendsepmodel_adj_binsepmodel_adj_contsepmodel_binsepmodel_contsim_studysim_study_parsplines_cal_contsplines_contsw_trendtimemachine_bintimemachine_cont

Dependencies:abindassertthatbackportsbayesplotBayesPPDBHbootcallrcheckmateclicodacodetoolscpp11descdigestdistributionaldoFuturedplyrfarverforeachFormulafuturefuture.applygenericsgeometryggplot2ggridgesglobalsgluegmpgridExtragtableinlineisobanditeratorsjsonlitelabelinglatticelifecyclelinproglistenvlme4lmerTestloolpSolvemagicmagrittrMASSMatrixmatrixStatsmgcvminqamvtnormnlmenloptrnumDerivotelparallellypbapplypillarpkgbuildpkgconfigplyrposteriorprocessxproxypspurrrQuickJSRR6RBesTrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppNumericalRcppParallelRcppProgressRdpackreformulasregistryreshape2rjagsrlangROIrstanrstantoolsS7scalesslamspaMMStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

NCC Introduction
Non-concurrent controls in platform trials
Rendered fromncc_intro.Rmdusingknitr::rmarkdown

Last update: 2023-06-15
Started: 2022-12-13

How to install the NCC package
1) Installing JAGS | 2) Installing the NCC package | 2a) from CRAN | 2b) from GitHub
Rendered frominstallation.Rmdusingknitr::rmarkdown

Last update: 2023-05-16
Started: 2023-05-15

How to run a simulation study
Preparing scenarios | Running simulations | Simulation results | Type I error | Bias | MSE
Rendered fromhow_to_run_sim_study.Rmdusingknitr::rmarkdown

Last update: 2023-02-19
Started: 2022-12-13

How to simulate binary data
datasim_bin() | Assumptions | Notation | Usage | Input | Output | Examples
Rendered fromdatasim_bin.Rmdusingknitr::rmarkdown

Last update: 2022-12-13
Started: 2022-03-09

How to simulate continuous data
datasim_cont() | Assumptions | Notation | Usage | Input | Output | Examples
Rendered fromdatasim_cont.Rmdusingknitr::rmarkdown

Last update: 2022-12-13
Started: 2022-03-07

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 binary data from a platform trial with a shared control arm and a given number of experimental treatment arms entering at given time points using a user-specified sample size matrixdatasim_bin_2
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
Frequentist linear regression model analysis for continuous data with linear adjustment for timefixmodel_lin_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
Analysis for continuous data using the MAP Prior approachMAPpriorNew_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
Mixed regression model analysis for continuous data using the covariates treatment and calendar time unit as fixed effects and the interaction between them as a random effectmixmodel_int_cal_cont
Mixed regression model analysis for continuous data using the covariates treatment and period as fixed effects and the interaction between them as a random effectmixmodel_int_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
Analysis for continuous data using the power prior approachpowerprior_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