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Lu N, Chen WC, Li H, Song C, Tiwari R, Wang C, Xu Y, Yue LQ. Propensity score-incorporated adaptive design approaches when incorporating real-world data. Pharm Stat 2024; 23:204-218. [PMID: 38014753 DOI: 10.1002/pst.2347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 09/07/2023] [Accepted: 10/23/2023] [Indexed: 11/29/2023]
Abstract
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.
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Affiliation(s)
- Nelson Lu
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Wei-Chen Chen
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Heng Li
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Changhong Song
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ram Tiwari
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Lawrence Township, New Jersey, USA
| | - Chenguang Wang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yunling Xu
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Lilly Q Yue
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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Lu N, Wang C, Chen WC, Li H, Song C, Tiwari R, Xu Y, Yue LQ. Propensity score-integrated power prior approach for augmenting the control arm of a randomized controlled trial by incorporating multiple external data sources. J Biopharm Stat 2021; 32:158-169. [PMID: 34756158 DOI: 10.1080/10543406.2021.1998098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In this paper, a propensity score-integrated power prior approach is developed to augment the control arm of a two-arm randomized controlled trial (RCT) with subjects from multiple external data sources such as real-world data (RWD) and historical clinical studies containing subject-level outcomes and covariates. The propensity scores for the subjects in the external data sources versus the subjects in the RCT are first estimated, and then subjects are placed in different strata based on their estimated propensity scores. Within each propensity score stratum, a power prior is formulated with the information contributed by the external data sources, and Bayesian inference on the treatment effect is obtained. The proposed approach is implemented under the two-stage study design framework utilizing the outcome-free principle to ensure the integrity of a study. An illustrative example is provided to demonstrate the implementation of the proposed approach.
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Affiliation(s)
- Nelson Lu
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Chenguang Wang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Wei-Chen Chen
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Heng Li
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Changhong Song
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ram Tiwari
- 18426 Polynesian Lane, Boyds, Maryland 20841
| | - Yunling Xu
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Lilly Q Yue
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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Lu N, Wang C, Chen WC, Li H, Song C, Tiwari R, Xu Y, Yue LQ. Leverage multiple real-world data sources in single-arm medical device clinical studies. J Biopharm Stat 2021; 32:107-123. [PMID: 33844621 DOI: 10.1080/10543406.2021.1897994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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.
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Affiliation(s)
- Nelson Lu
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Chenguang Wang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Wei-Chen Chen
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Heng Li
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Changhong Song
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ram Tiwari
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Yunling Xu
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Lilly Q Yue
- Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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