Meta-analysis of blinded and unblinded studies for ongoing aggregate safety monitoring and evaluation
During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and...
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| Published in | Contemporary clinical trials Vol. 95; p. 106068 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.08.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1551-7144 1559-2030 1559-2030 |
| DOI | 10.1016/j.cct.2020.106068 |
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| Abstract | During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and EMA) compel sponsors for assessment of safety based on aggregate data. To identify and characterize the risks of medicinal products at a program level in a more timely and informed manner, aggregate safety evaluations should combine all available information, including from ongoing blinded trials, completed unblinded trials, and other data sources. In this article, we propose two Bayesian meta-analytic approaches for synthesizing blinded and unblinded studies in order to characterize the evolving safety profile of medicinal products at the program level. With the proposed approaches, sponsors can dynamically update knowledge of their product safety profiles as data accrue. Application of the procedures to a real and a hypothetical clinical trial program are provided to illustrate how the proposed approaches can be used to analyze a pre-specified event of interest and to screen for risk-elevated events. |
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| AbstractList | During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and EMA) compel sponsors for assessment of safety based on aggregate data. To identify and characterize the risks of medicinal products at a program level in a more timely and informed manner, aggregate safety evaluations should combine all available information, including from ongoing blinded trials, completed unblinded trials, and other data sources. In this article, we propose two Bayesian meta-analytic approaches for synthesizing blinded and unblinded studies in order to characterize the evolving safety profile of medicinal products at the program level. With the proposed approaches, sponsors can dynamically update knowledge of their product safety profiles as data accrue. Application of the procedures to a real and a hypothetical clinical trial program are provided to illustrate how the proposed approaches can be used to analyze a pre-specified event of interest and to screen for risk-elevated events. During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and EMA) compel sponsors for assessment of safety based on aggregate data. To identify and characterize the risks of medicinal products at a program level in a more timely and informed manner, aggregate safety evaluations should combine all available information, including from ongoing blinded trials, completed unblinded trials, and other data sources. In this article, we propose two Bayesian meta-analytic approaches for synthesizing blinded and unblinded studies in order to characterize the evolving safety profile of medicinal products at the program level. With the proposed approaches, sponsors can dynamically update knowledge of their product safety profiles as data accrue. Application of the procedures to a real and a hypothetical clinical trial program are provided to illustrate how the proposed approaches can be used to analyze a pre-specified event of interest and to screen for risk-elevated events.During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and EMA) compel sponsors for assessment of safety based on aggregate data. To identify and characterize the risks of medicinal products at a program level in a more timely and informed manner, aggregate safety evaluations should combine all available information, including from ongoing blinded trials, completed unblinded trials, and other data sources. In this article, we propose two Bayesian meta-analytic approaches for synthesizing blinded and unblinded studies in order to characterize the evolving safety profile of medicinal products at the program level. With the proposed approaches, sponsors can dynamically update knowledge of their product safety profiles as data accrue. Application of the procedures to a real and a hypothetical clinical trial program are provided to illustrate how the proposed approaches can be used to analyze a pre-specified event of interest and to screen for risk-elevated events. |
| ArticleNumber | 106068 |
| Author | Yuan, Shuai Sammy Lin, Li-An Li, Lie Ball, Greg |
| Author_xml | – sequence: 1 givenname: Li-An surname: Lin fullname: Lin, Li-An email: li.an.lin@merck.com organization: Clinical Safety Statistics, Merck & Co., Inc., Rahway, NJ, USA – sequence: 2 givenname: Shuai Sammy surname: Yuan fullname: Yuan, Shuai Sammy organization: Biometrics Team, Kite Pharma, a Gilead Company, Santa Monica, CA, USA – sequence: 3 givenname: Lie surname: Li fullname: Li, Lie organization: Clinical Safety Statistics, Merck & Co., Inc., North Wales, PA, USA – sequence: 4 givenname: Greg surname: Ball fullname: Ball, Greg organization: Clinical Safety Statistics, Merck & Co., Inc., Rahway, NJ, USA |
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| Cites_doi | 10.3390/pharmaceutics5010094 10.1177/096228020101000404 10.1080/01621459.1994.10476892 10.1016/j.cct.2011.05.008 10.1002/sim.7073 10.1177/2168479018778282 10.1177/2168479016656702 10.1080/10543406.2010.520181 10.1002/sim.7129 10.1136/amiajnl-2012-001257 10.1016/0021-9681(76)90044-8 10.1002/sim.2528 10.7326/0003-4819-107-2-224 10.18637/jss.v076.i01 10.1002/pst.1898 10.1080/19466315.2017.1409134 10.1080/19466315.2015.1117017 10.2307/2533377 10.1002/sim.1186 10.1177/0962280214534409 10.1007/s43441-020-00183-7 10.18637/jss.v036.i03 10.1007/s43441-019-00076-4 10.1111/j.1541-0420.2011.01564.x 10.2165/00002018-199308030-00006 10.1016/S0304-3959(99)00302-4 10.1002/(SICI)1097-0258(19990215)18:3<321::AID-SIM28>3.0.CO;2-P 10.1016/j.cct.2019.06.022 |
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| Keywords | Safety monitoring and evaluation Blinded analysis Meta analysis Unblinded analysis Evidence synthesis |
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