Increasing efficiency for estimating treatment-biomarker interactions with historical data.
Increasing efficiency for estimating treatment-biomarker interactions with historical data.
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DOI：
10.1177/0962280214535370
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发表时间：
2016-12
影响因子：
2.3
通讯作者：
Mukherjee B
中科院分区：
文献类型：
--
作者：
Boonstra PS;Taylor JM;Mukherjee B
在样本量较小的II期试验中，检测治疗-生物标记物相互作用是一项更适合大样本量的任务，具有挑战性。在这篇文章中，我们调查了两个看似可行的历史数据来源如何包含部分信息，以帮助在随机第二阶段研究中估计治疗-生物标记物相互作用参数。这两个历史数据集中的参数都不是单独确定的；尽管如此，这两个数据集中都可以提供有关参数的一些信息，从而提高其估计的精度。为了说明提高效率的潜力和对研究设计的影响，我们考虑了高斯结果和生物标记物数据，并使用预期的Fisher信息矩阵计算渐近方差。我们通过数值研究和在简化的环境中，从问题的代数发展中得出的见解来量化效率方面的收益。我们发现，即使历史数据和预期数据不是来自相同的基础模型，在精度方面也可能有不可忽略的增长。
Detecting a treatment-biomarker interaction, which is a task better suited for large sample sizes, in a phase II trial, which has a small sample size, is challenging. In this paper, we investigate how two plausibly-available sources of historical data may contain partial information to help estimate the treatment-biomarker interaction parameter in a randomized phase II study. The parameter is not identified in either historical dataset alone; nonetheless, both can provide some information about the parameter and, consequently, increase the precision of its estimate. To illustrate the potential for gains in efficiency and implications for the design of the study, we consider Gaussian outcomes and biomarker data and calculate the asymptotic variance using the expected Fisher information matrix. We quantify the gain in efficiency both through a numerical study and, in a simplified setting, insights derived from an algebraic development of the problem. We find that a non-negligible gain in precision is possible, even if the historical and prospective data do not arise from identical underlying models.