Propensity score-incorporated adaptive design approaches when incorporating real-world data
© 2023 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
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| Enthalten in: |
Pharmaceutical statistics - 23(2024), 2 vom: 19. März, Seite 204-218 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lu, Nelson [VerfasserIn] |
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| Links: |
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| Themen: |
Adaptive design |
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| Anmerkungen: |
Date Completed 08.03.2024 Date Revised 08.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/pst.2347 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365059528 |
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| 520 | |a © 2023 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Fisher's combination test | |
| 650 | 4 | |a PSCL | |
| 650 | 4 | |a RWD | |
| 650 | 4 | |a RWE | |
| 650 | 4 | |a adaptive design | |
| 650 | 4 | |a composite likelihood | |
| 650 | 4 | |a outcome-free design | |
| 650 | 4 | |a propensity score | |
| 650 | 4 | |a real-world data | |
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| 700 | 1 | |a Chen, Wei-Chen |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Heng |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Changhong |e verfasserin |4 aut | |
| 700 | 1 | |a Tiwari, Ram |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chenguang |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Yunling |e verfasserin |4 aut | |
| 700 | 1 | |a Yue, Lilly Q |e verfasserin |4 aut | |
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