Plug-stat® : a cloud-based application to facilitate the emulation of clinical trials for real-world evidence based on real-world data

Exploitation of the ever-increasing volume of observational data has become a major challenge for real-world evidence (RWE). Causal inference can be viewed as emulations of clinical trials (ECTs) from real-world data (RWD). This requires in-depth knowledge of data management and modern statistical models, which evolve considerably. We have developed Plug-Stat (R) to facilitate these analyses. Plug-Stat (R) is a user-friendly graphical interface of R software implemented with JavaScript and VB.net . The implementation of Plug-Stat (R) for a specific database consists of two main tasks. The first concerns the automatic importation of data to link the software with the data and to avoid the repetition of data management activities for each study. The second is to define the outcomes and the associated possible confounders. To perform an ECT, we proposed the use of inverse probability weighting, flexible parametric estimation of the propensity score model with B-splines, and robust estimation of the variance. The statistical modeling steps are automatized to direct the user toward the correct methodology without data management. We illustrated the usefulness of Plug-Stat (R) with several recently published ECTs. In this paper, we presented the outcome comparison of preemptive deceased-donor kidney transplantations versus transplantations after dialysis. The interactive interfaces assist the user in conducting causal research based on ECT, and tutorials are available, offering self-training opportunities. Plug-Stat (R) reduces the time and, consequently, the cost necessary for different studies from the same cohort.

Automated summary

This paper discusses the challenges of utilizing observational data for real-world evidence (RWE) and proposes a tool called Plug-Stat (R) to facilitate causal inference analysis of real-world data. The tool features a user-friendly graphical interface and automated statistical modeling steps, providing time and cost reduction.