Assessing a composite end point for new tocolytics in clinical trials: Data from 4 US integrated delivery networks
Purpose A composite end point (CE) measuring neonatal benefit was created for use in tocolytic randomized controlled trials with rates assessed using data from one referral hospital. The goal of this study was to assess wider generalizability of the CE, using data from multiple integrated delivery n...
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Veröffentlicht in: | Pharmacoepidemiology and drug safety 2018-02, Vol.27 (2), p.213-220 |
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Sprache: | eng |
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Zusammenfassung: | Purpose
A composite end point (CE) measuring neonatal benefit was created for use in tocolytic randomized controlled trials with rates assessed using data from one referral hospital. The goal of this study was to assess wider generalizability of the CE, using data from multiple integrated delivery networks, creating a cohort of linked mother‐neonate pairs to understand neonatal outcomes in a broad population.
Methods
Retrospective data on births (2001‐2012) were collected from 4 US integrated delivery networks in the COMparative effectiveness PAtient Safety and Surveillance (COMPASS) Research Network, and linked mother‐neonate pairs were identified. The CE was analyzed for all in‐hospital singleton neonates at ≥24 weeks of gestational age (GA) born to mothers aged ≤45 years at a referral hospital or hospital with >2000 annual births.
Results
The CE analyses included 56 485 eligible mother‐neonate pairs; frequency of the CE decreased from 89% to 66% between GA weeks 24 and 29 and further decreased to 34 weeks of GA. Composite end point rates were 20% to 30% lower at 24 to 30 weeks of GA in COMPASS compared with Medical University of South Carolina but were similar by 31 weeks.
Conclusions
The COMPASS Network enabled evaluation of the CE across a large population demonstrating that the CE findings could be replicated beyond a single hospital and the potential for lower CE frequency. Based on this, an adaptive design was adopted for randomized controlled trials, specifically sample size reestimation to mitigate against the risk of lower outcome rates, highlighting the use of real‐world data in drug development. |
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ISSN: | 1053-8569 1099-1557 |
DOI: | 10.1002/pds.4371 |