Warner Jeremy L, Yang Peter C, Alterovitz Gil
Jeremy L. Warner, Vanderbilt University, Nashville, TN; Peter C. Yang, Massachusetts General Hospital; and Gil Alterovitz, Harvard Medical School and Harvard-Massachusetts Institute of Technology Division of Health Science, Boston; and Massachusetts Institute of Technology, Cambridge, MA.
JCO Clin Cancer Inform. 2017 Nov;1:1-9. doi: 10.1200/CCI.17.00079.
Despite the plethora of randomized controlled trial (RCT) data, most cancer treatment recommendations are formulated by experts. Alternatively, network meta-analysis (NMA) is one method of analyzing multiple indirect treatment comparisons. However, NMA does not account for mixed end points or temporality. Previously, we described a prototype information theoretical approach for the construction of ranked chemotherapy treatment regimen networks. Here, we propose modifications to overcome an apparent straw man effect, where the most studied regimens were the most negatively valued.
RCTs from two scenarios-upfront treatment of chronic myelogenous leukemia and relapsed/refractory multiple myeloma-were assembled into ranked networks using an automated algorithm based on effect sizes, statistical significance, surrogacy of end points, and time since RCT publication. Vertex and edge color, transparency, and size were used to visually analyze the network. This analysis led to the additional incorporation of value propagation.
A total of 18 regimens with 42 connections (chronic myelogenous leukemia) and 28 regimens with 25 connections (relapsed/refractory multiple myeloma) were analyzed. An initial negative correlation between vertex value and size was ameliorated after value propagation, although not eliminated. Updated rankings were in close agreement with published guidelines and NMAs.
Straw man effects can distort the comparative efficacy of newer regimens at the expense of older regimens, which are often cheaper or less toxic. Using an automated method, we ameliorated this effect and produced rankings consistent with common practice and published guidelines in two distinct cancer settings. These findings are likely to be generalizable and suggest a new means of ranking efficacy in cancer trials.
尽管有大量随机对照试验（RCT）数据，但大多数癌症治疗建议是由专家制定的。另外，网络荟萃分析（NMA）是分析多个间接治疗比较的一种方法。然而，NMA没有考虑混合终点或时间顺序。此前，我们描述了一种用于构建化疗治疗方案排名网络的信息理论原型方法。在此，我们提出修改以克服一种明显的稻草人效应，即研究最多的方案被赋予最负面的价值。
来自两种情况（慢性粒细胞白血病的一线治疗和复发/难治性多发性骨髓瘤）的RCT使用基于效应大小、统计显著性、终点替代指标以及自RCT发表以来的时间的自动算法组装成排名网络。顶点和边的颜色、透明度和大小用于直观地分析网络。这种分析导致额外纳入了价值传播。
共分析了18种方案，有42个连接（慢性粒细胞白血病）以及28种方案，有25个连接（复发/难治性多发性骨髓瘤）。价值传播后，顶点值与大小之间最初的负相关有所改善，尽管并未消除。更新后的排名与已发表的指南和NMA密切一致。
稻草人效应可能会以牺牲通常更便宜或毒性更小的旧方案为代价，扭曲新方案的比较疗效。通过使用一种自动化方法，我们改善了这种效应，并在两种不同的癌症背景下得出了与常规做法和已发表指南一致的排名。这些发现可能具有普遍性，并提示了一种在癌症试验中对疗效进行排名的新方法。
