Novel statistical approaches and applications in leveraging real-world data in regulatory clinical studies

Abstract

In medical product development, there has been a growing interest in utilizing real-world data which have become abundant owing to advances in biomedical science, information technology and engineering. High-quality real-world data may be utilized to generate real-world evidence for regulatory or healthcare decision-making. We discuss propensity score-based approaches for leveraging patients from a real-world data source to construct a control group for a non-randomized comparative study or to augment a single-arm or randomized prospective investigational clinical study. The proposed propensity score-based approaches leverage real-world patients that are similar to those prospectively enrolled into the investigational clinical study in terms of baseline characteristics. Either frequentist or Bayesian inference can then be applied for outcome data analysis, with the option of down-weighting information from the real-world data source. Examples based on pre-market regulatory review experience are provided to illustrate the implementation of the proposed approaches.