Abstract
In medical product development, there has been an increased interest in utilizing real-world data which have become abundant with recent advances in biomedical science, information technology, and engineering. High-quality real-world data may be analyzed to generate real-world evidence that can be utilized in the regulatory and healthcare decision-making. In this paper, we consider the case in which a single-arm clinical study, viewed as the primary data source, is supplemented with patients from a real-world data source containing both clinical outcome and covariate data at the patient-level. Propensity score methodology is used to identify real-world data patients that are similar to those in the single-arm study in terms of the baseline characteristics, and to stratify these patients into strata based on the proximity of the propensity scores. In each stratum, a composite likelihood function of a parameter of interest is constructed by down-weighting the information from the real-world data source, and an estimate of the stratum-specific parameter is obtained by maximizing the composite likelihood function. These stratum-specific estimates are then combined to obtain an overall population-level estimate of the parameter of interest. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example based on our experience is provided to illustrate the implementation of the proposed approach.
Original language | English (US) |
---|---|
Pages (from-to) | 495-507 |
Number of pages | 13 |
Journal | Journal of biopharmaceutical statistics |
Volume | 30 |
Issue number | 3 |
DOIs | |
State | Published - May 3 2020 |
Keywords
- Covariate balance
- PSCL
- composite likelihood
- overlapping coefficient
- propensity score
- real-world data
- real-world evidence
ASJC Scopus subject areas
- Statistics and Probability
- Pharmacology
- Pharmacology (medical)