Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Global Biometrics and Data Sciences, Bristol Myers Squibb, Lawrence Township, New Jersey, USA.
Pharm Stat. 2024 Mar-Apr;23(2):204-218. doi: 10.1002/pst.2347. Epub 2023 Nov 28.
The propensity score-integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real-world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources are placed in the same stratum, and then composite likelihood method is applied to down-weight the information from the RWD. While PSCL was originally proposed for a fixed design, it can be extended to be applied under an adaptive design framework with the purpose to either potentially claim an early success or to re-estimate the sample size. In this paper, a general strategy is proposed due to the feature of PSCL. For the possibility of claiming early success, Fisher's combination test is utilized. When the purpose is to re-estimate the sample size, the proposed procedure is based on the test proposed by Cui, Hung, and Wang. The implementation of these two procedures is demonstrated via an example.
倾向评分综合复合似然（PSCL）方法是一种可用于设计和分析应用的方法，当利用真实世界数据（RWD）来增强前瞻性设计的临床研究时，可以采用这种方法。在 PSCL 中，根据倾向评分（PS）形成分层，以便将当前研究和 RWD 来源的基线协变量相似的受试者放置在同一分层中，然后应用复合似然方法来降低 RWD 信息的权重。虽然 PSCL 最初是为固定设计提出的，但可以将其扩展到自适应设计框架下应用，目的是要么潜在地宣称早期成功，要么重新估计样本量。在本文中，由于 PSCL 的特点，提出了一种通用策略。对于宣称早期成功的可能性，使用 Fisher 组合检验。当目的是重新估计样本量时，所提出的程序基于 Cui、Hung 和 Wang 提出的检验。通过一个例子演示了这两个程序的实现。
