Propensity score-integrated composite likelihood approach for incorporating real-world evidence in single-arm clinical studies

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

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)495-507
Number of pages13
JournalJournal of biopharmaceutical statistics
Volume30
Issue number3
DOIs
StatePublished - 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)

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