Chang Mark, Wang Jing
a AMG Pharmaceuticals, Inc ., Lexington , Massachusetts , USA.
J Biopharm Stat. 2015;25(5):1039-64. doi: 10.1080/10543406.2014.971164. Epub 2014 Oct 20.
In a classical drop-loser (or drop-arm) design, patients are randomized into all arms (doses) and at the interim analysis, inferior arms are dropped. Therefore, compared to the traditional dose-finding design, this adaptive design can reduce the sample size by not carrying over all doses to the end of the trial or dropping the losers earlier. However, all the doses have to be explored. For unimodal (including linear or umbrella) response curves, we proposed an effective dose-finding design that allows adding arms at the interim analysis. The trial design starts with two arms, depending on the response of the two arms and the unimodality assumption; we can decide which new arms to be added. This design does not require exploring all arms (doses) to find the best responsive dose; therefore, it can further reduce the sample size from the drop-loser design by as much as 10-20%.
在经典的淘汰失败者(或垂臂)设计中,患者被随机分配到所有组(剂量组),并在期中分析时淘汰疗效较差的组。因此,与传统的剂量探索设计相比,这种适应性设计可以通过不将所有剂量组保留到试验结束或更早淘汰失败者来减少样本量。然而,所有剂量都必须进行探索。对于单峰(包括线性或伞形)反应曲线,我们提出了一种有效的剂量探索设计,该设计允许在期中分析时增加组。试验设计从两组开始,根据这两组的反应以及单峰假设,我们可以决定添加哪些新组。这种设计不需要探索所有组(剂量组)来找到最佳反应剂量;因此,它可以比淘汰失败者设计进一步减少多达10%至20%的样本量。