NCC
package allows users to simulate platform trials and to compare arms using nonconcurrent control data.
Design overview
We consider a platform trial evaluating the efficacy of K treatment arms compared to a shared control. We assume that treatment arms enter the platform trial sequentially. In particular, we consider a trial starting with at least one initial treatment arm, where a new arm is added after every d = (d_{1},...,d_{K}) patients have been recruited to the trial (with d_{1} = 0).
We divide the duration of the trial into S periods, where the periods are the time intervals bounded by times at which a treatment arm either enters or leaves the platform.
The below figure illustrates the considered trial design.
Functions
This package contains the following functions:
Data generation
Main functions for data generation

datasim_bin()
simulates data with binary outcomes 
datasim_cont()
simulates data with continuous outcomes
Auxiliary functions for data generation

get_ss_matrix()
computes sample sizes per arm and period 
linear_trend()
is the linear time trend function, used to generate the trend for each patient 
sw_trend()
is the stepwise time trend function, used generate the trend for each patient 
inv_u_trend()
is the invertedu time trend function, used generate the trend for each patient 
seasonal_trend()
is the seasonal time trend function, used generate the trend for each patient
Data analysis
Treatmentcontrol comparisons for binary endpoints
Frequentist approaches

fixmodel_bin()
performs analysis using a regression model adjusting for periods 
fixmodel_cal_bin()
performs analysis using a regression model adjusting for calendar time 
poolmodel_bin()
performs pooled analysis 
sepmodel_bin()
performs separate analysis 
sepmodel_adj_bin()
performs separate analysis adjusting for periods
Bayesian approaches

MAPprior_bin()
performs analysis using the MAP prior approach 
timemachine_bin()
performs analysis using the Time Machine approach
Treatmentcontrol comparisons for continuous endpoints
Frequentist approaches

fixmodel_cont()
performs analysis using a regression model adjusting for periods 
fixmodel_cal_cont()
performs analysis using a regression model adjusting for calendar time 
gam_cont()
performs analysis using generalized additive model 
mixmodel_cont()
performs analysis using a mixed model adjusting for periods as a random factor 
mixmodel_cal_cont()
performs analysis using a mixed model adjusting for calendar time as a random factor 
mixmodel_AR1_cont()
performs analysis using a mixed model adjusting for periods as a random factor with AR1 correlation structure 
mixmodel_AR1_cal_cont()
performs analysis using a mixed model adjusting for calendar time as a random factor with AR1 correlation structure 
piecewise_cont()
performs analysis using discontinuous piecewise polynomials per period 
piecewise_cal_cont()
performs analysis using discontinuous piecewise polynomials per calendar time 
poolmodel_cont()
performs pooled analysis 
sepmodel_cont()
performs separate analysis 
sepmodel_adj_cont()
performs separate analysis adjusting for periods 
splines_cont()
performs analysis using regression splines with knots placed according to periods 
splines_cal_cont()
performs analysis using regression splines with knots placed according to calendar times
Bayesian approaches

MAPprior_cont()
performs analysis using the MAP prior approach 
timemachine_cont()
performs analysis using the Time Machine approach
Running simulations

all_models()
is an auxiliary wrapper function to analyze given dataset (treatmentcontrol comparisons) with multiple models 
sim_study()
is a wrapper function to run a simulation study (treatmentcontrol comparisons) for desired scenarios 
sim_study_par()
is a wrapper function to run a simulation study (treatmentcontrol comparisons) for desired scenarios in parallel
Visualization

plot_trial()
visualizes the progress of a simulated trial
For a more detailed description of the functions, see the vignettes in the Rpackage website (https://pavlakrotka.github.io/NCC/).
Scheme of the package structure
The below figure illustrates the NCC
package functions by functionality.
Installation
Please note that prior to installing the NCC
package, the JAGS library needs to be installed on your computer.
To install the stable version of the NCC
package from CRAN, please run the following code:
install.packages("NCC")
To install the latest development version of the NCC
package from GitHub, please run the following code:
# install.packages("devtools")
devtools::install_github("pavlakrotka/NCC", build_vignettes = TRUE)
For further details regarding the package installation, see https://pavlakrotka.github.io/NCC/articles/installation.html.
Documentation
Documentation of all functions as well as vignettes with further description and examples can be found at the package website: https://pavlakrotka.github.io/NCC/
References
[1] Krotka, P., Hees, K., et al. “NCC: An Rpackage for analysis and simulation of platform trials with nonconcurrent controls.” arXiv preprint (2023): arXiv:2302.12634
[2] Bofill Roig, M., Krotka, P., et al. “On modelbased time trend adjustments in platform trials with nonconcurrent controls.” BMC medical research methodology 22.1 (2022): 116.
[3] Lee, K. M., and Wason, J. “Including nonconcurrent control patients in the analysis of platform trials: is it worth it?.” BMC medical research methodology 20.1 (2020): 112.
[4] Saville, B. R., Berry, D. A., et al. “The Bayesian Time Machine: Accounting for Temporal Drift in Multiarm Platform Trials.” Clinical Trials 19.5 (2022): 490501
Funding
EUPEARL (EU PatientcEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development nonprofit organisation, Spring works Therapeutics Inc. This publication reflects the authors’ views. Neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.