Abstract
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 language | English (US) |
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Pages (from-to) | 107-123 |
Number of pages | 17 |
Journal | Journal of biopharmaceutical statistics |
Volume | 32 |
Issue number | 1 |
DOIs | |
State | Published - 2022 |
Keywords
- 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)