Here, you can find the code to run the models and the simulations presented in the article entitled “Random-effects meta-analysis of Phase I dose-finding studies using stochastic process priors”. Link: https://clicktime.symantec.com/3MnsciFmpCx2GTRRtjmLJoD6H2?u=https%3A%2F%2Farxiv.org%2Fabs%2F1908.06733

The scripts can be run in all operating system. R must be installed with the following libraries: rstan (we used rstan_2.17.3), UBCRM, MASS, dfcrm, Iso, ggplot2, ggrepel, gridExtra, parallel, dfmeta, xtable.

We worked on the following environment: R version 3.5.0 Platform: macOS High Sierra 10.13.6

Folders:

• stan_models: the Stan models needed to compute all proposed methods are grouped in this folder. The user must to add them in the same folder of the R analysis session.

  • MADF.stan;
  • MADF1.stan;
  • MADF2.stan;
  • MADF3.stan (to note, this is used also for MADF4).

• data: data used in the work are collected in this folder.

  • sorafenib2.csv;
  • irinotecan.csv.

• scripts: R scripts used to run methods and simulations.

  • scenario_generation.r: to generate datasets in a pre-specified scenario (see details in the script);
  • scenario_npatients.r: to count the number of patients allocated to each dose at each run;
  • MADF_sim.R: to run simulation using MADF method;
  • MADF1_sim.R: to run simulation using MADF1 method;
  • MADF2_sim.R: to run simulation using MADF2 method;
  • MADF3-4_sim.R: to run simulation using MADF3 and MADF4 method;
  • ZKO_sim.R: to run simulation using ZKO method;
  • read_results.R: to create the results table with percentage of final MTD selection;
  • plot_prior_induced.R: to run plot the induced priors and ESS on fixed effects;
  • Estimation_Examples.R: to run MADF on the datasets in data folder.

For any issue, please contact moreno.ursino@gmail.com.