Mohsen Soltanifar

SimSST: An R Statistical Software Package to Simulate Stop Signal Task Data

The stop signal task (SST) paradigm with its original roots in 1948 has been proposed to study humans’ response inhibition. Several statistical software codes have been designed by researchers to simulate SST data in order to study various theories of modeling response inhibition and their assumptions. Yet, there has been a missing standalone statistical software package to enable researchers to simulate SST data under generalized scenarios. This paper presents the R statistical software package “SimSST”, available in Comprehensive R Archive Network (CRAN), to simulate stop signal task (SST) data. The package is based on the general non-independent horse race model, the copulas in probability theory, and underlying ExGaussian (ExG) or Shifted Wald (SW) distributional assumption for the involving go and stop processes enabling the researchers to simulate sixteen scenarios of the SST data. A working example for one of the scenarios is presented to evaluate the simulations’ precision on parameter estimations. Package limitations and future work directions for its subsequent extensions are discussed.

Mohsen Soltanifar headshot
Pronouns: he/him
Vancouver, BC, Canada
Mohsen Soltanifar is currently Senior Biostatistican at ClinChoice and an adjunct lecturer at Northeastern University in Vancouver, BC, Canada. He has 2+ years experience in CRO/Pharma and 8+ years experience in Healthcare. His main area of interest in statistics is Clinical Trials with focus of R software applications in their design, analysis, and result presentations. He got his PhD in Biostatistics from University of Tornoto in Canada in 2020 and as of that year has served as registered reviewer for 15+ journals including "Current Oncology" and "Clinical and Translational Neurosicence(CTN)".