NCC: Simulation and analysis of platform trials with non-concurrent controls

NCC package allows users to simulate platform trials and to compare
arms using non-concurrent 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 $\mathbf{d}=(d_1,...,d_K)$ 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 step-wise time trend function, used generate the
  trend for each patient
• inv_u_trend() is the inverted-u 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

Treatment-control 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

Treatment-control 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
• powerprior_cont() performs analysis using the power prior approach

Running simulations

• all_models() is an auxiliary wrapper function to analyze given
  dataset (treatment-control comparisons) with multiple models
• sim_study() is a wrapper function to run a simulation study
  (treatment-control comparisons) for desired scenarios
• sim_study_par() is a wrapper function to run a simulation study
  (treatment-control 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 R-package 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 R-package for analysis and
simulation of platform trials with non-concurrent
controls.” SoftwareX 23 (2023): 101437.

[2] Bofill Roig, M., Krotka, P., et al. “On model-based time trend
adjustments in platform trials with non-concurrent
controls.”
BMC medical research methodology 22.1 (2022): 1-16.

[3] Lee, K. M., and Wason, J. “Including non-concurrent control
patients in the analysis of platform trials: is it worth
it?.”
BMC medical research methodology 20.1 (2020): 1-12.

[4] Saville, B. R., Berry, D. A., et al. “The Bayesian Time Machine:
Accounting for Temporal Drift in Multi-arm Platform
Trials.”
Clinical Trials 19.5 (2022): 490-501

Funding

EU-PEARL (EU Patient-cEntric 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
non-profit 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.