Leverage multiple real-world data sources in single-arm medical device clinical studies

Nelson Lu, Chenguang Wang, Wei Chen Chen, Heng Li, Changhong Song, Ram Tiwari, Yunling Xu, Lilly Q. Yue

Research output: Contribution to journalArticlepeer-review


The interest in utilizing real-world data (RWD) has been considerably increasing in medical product development and evaluation. With proper usage and analysis of high-quality real-world data, real-world evidence (RWE) can be generated to inform regulatory and healthcare decision-making. This paper proposes a study design and data analysis approach for a prospective, single-arm clinical study that is supplemented with patients from multiple real-world data sources containing patient-level covariate and outcome data. After the amount of information to be borrowed from each real-world data source is determined, the propensity score-integrated composite likelihood method is applied to obtain an estimate of the parameter of interest based on data from the prospective clinical study and this real-world data source. This method is applied to each real-world data source. The final estimate of the parameter of interest is then obtained by taking a weighted average of all these estimates. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example is presented to illustrate how to implement the proposed approach.

Original languageEnglish (US)
Pages (from-to)107-123
Number of pages17
JournalJournal of biopharmaceutical statistics
Issue number1
StatePublished - 2022


  • Real-world data
  • composite likelihood
  • multiple data sources
  • outcome-free design
  • propensity score
  • pscl
  • real-world evidence
  • rwd
  • rwe

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)


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