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Lessons Learned From Multi-regional Trials With Signals of Treatment Effect Heterogeneity
Inconsistent results across regions have been reported in a number of recent large trials. In this research, by reviewing results from studies that showed inconsistent treatment effects, and summarizing lessons learned, we provide some recommendations for minimizing the chance of inconsistency and a...
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Published in: | Therapeutic innovation & regulatory science 2018-10, p.2168479018805428 |
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creator | Li, Gang Quan, Hui Lan, Gordon Ouyang, Soo Peter Chen, Fei Robieson, Weining Wang, William Binkowitz, Bruce Yuan, Shuai Sammy Tanaka, Yoko Chen, Josh Matsuoka, Nobushige Zhang, Lanju Yang, Song Gallo, Paul |
description | Inconsistent results across regions have been reported in a number of recent large trials. In this research, by reviewing results from studies that showed inconsistent treatment effects, and summarizing lessons learned, we provide some recommendations for minimizing the chance of inconsistency and allowing more accurate interpretation when such signs of heterogeneity arise, for example: keep the number of regions for consistency evaluation at a minimum to avoid observing false inconsistency signals; proactively address in the protocol the differences in culture, medical practices, and other factors that are potentially different across regions; closely monitor the blinded data from early-enrolled patients to more effectively identify and address issues such as imbalance of baseline covariates or inconsistency of primary outcome rates across regions. For treatments of life-threatening conditions, the stakes for accurate interpretation of MRCT results are high; the criteria for decisions warrant careful consideration. |
doi_str_mv | 10.1177/2168479018805428 |
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In this research, by reviewing results from studies that showed inconsistent treatment effects, and summarizing lessons learned, we provide some recommendations for minimizing the chance of inconsistency and allowing more accurate interpretation when such signs of heterogeneity arise, for example: keep the number of regions for consistency evaluation at a minimum to avoid observing false inconsistency signals; proactively address in the protocol the differences in culture, medical practices, and other factors that are potentially different across regions; closely monitor the blinded data from early-enrolled patients to more effectively identify and address issues such as imbalance of baseline covariates or inconsistency of primary outcome rates across regions. 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title | Lessons Learned From Multi-regional Trials With Signals of Treatment Effect Heterogeneity |
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