Improving clinical trial efficiency by biomarker-guided patient selection
Improving clinical trial efficiency by biomarker-guided patient selection
复制标题
DOI：
10.1186/1745-6215-15-103
复制
发表时间：
2014-04-02
期刊：
影响因子：
2.5
通讯作者：
Roes, Kit C. B.
中科院分区：
文献类型：
--
作者：
Boessen, Ruud;Heerspink, Hiddo J. Lambers;Roes, Kit C. B.
背景：在许多治疗领域，已经确定了与不同治疗反应相关的个体患者标志物。这些标志物包括基线特征以及治疗后的短期变化。使用这样的预测标志物，以选择纳入随机临床试验的受试者可能会导致更有针对性的研究，并减少受试者的数量，以recruit.Methods：本研究比较了三个试验设计的样本量需要建立在一系列的现实情况下的治疗效果。传统的平行组设计作为参考点，而替代设计选择的受试者的基线特征或早期改善后，一个短的积极的磨合期。使用模型生成数据，该模型将治疗对生存期的影响表征为主要效应、与基线标志物的相互作用和/或早期标志物改善的组合。一个有代表性的方案来自经验dataevaluated.Results：模拟显示，积极的运行设计可以大大减少招募的受试者的数量时，积极的运行过程中的改善是一个可靠的预测差异治疗反应。在这种情况下，基线选择设计也比平行组设计更有效，但比同样限制人群的活性导入设计效率低。然而，对于大多数情况下，基线选择design的优势是limited.Conclusions：一个积极的磨合设计可以大大减少招募的随机临床试验的受试者的数量。然而，正如基线选择设计一样，结果的普遍性可能有限，实施可能困难。
Background: In many therapeutic areas, individual patient markers have been identified that are associated with differential treatment response. These markers include both baseline characteristics, as well as short-term changes following treatment. Using such predictive markers to select subjects for inclusion in randomized clinical trials could potentially result in more targeted studies and reduce the number of subjects to recruit.Methods: This study compared three trial designs on the sample size needed to establish treatment efficacy across a range of realistic scenarios. A conventional parallel group design served as the point of reference, while the alternative designs selected subjects on either a baseline characteristic or an early improvement after a short active run-in phase. Data were generated using a model that characterized the effect of treatment on survival as a combination of a primary effect, an interaction with a baseline marker and/or an early marker improvement. A representative scenario derived from empirical data was also evaluated.Results: Simulations showed that an active run-in design could substantially reduce the number of subjects to recruit when improvement during active run-in was a reliable predictor of differential treatment response. In this case, the baseline selection design was also more efficient than the parallel group design, but less efficient than the active run-in design with an equally restricted population. For most scenarios, however, the advantage of the baseline selection design was limited.Conclusions: An active run-in design could substantially reduce the number of subjects to recruit in a randomized clinical trial. However, just as with the baseline selection design, generalizability of results may be limited and implementation could be difficult.