Eaton Anne, Iasonos Alexia, Gounder Mrinal M, Pamer Erika G, Drilon Alexander, Vulih Diana, Smith Gary L, Ivy S Percy, Spriggs David R, Hyman David M
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York. Weill Cornell Medical College, New York, New York.
Clin Cancer Res. 2016 Feb 1;22(3):553-9. doi: 10.1158/1078-0432.CCR-15-0339. Epub 2015 Aug 31.
Phase I studies rely on investigators to accurately attribute adverse events as related or unrelated to study drug. This information is ultimately used to help establish a safe dose. Attribution in the phase I setting has not been widely studied and assessing the accuracy of attribution is complicated by the lack of a gold standard. We examined dose-toxicity relationships as a function of attribution and toxicity category to evaluate for evidence of toxicity misattribution.
Individual patient records from 38 phase I studies activated between 2000 and 2010 were used. Dose was defined as a percentage of maximum dose administered on each study. Relationships between dose and patient-level toxicity were explored graphically and with logistic regression. All P values were two-sided.
11,909 toxicities from 1,156 patients were analyzed. Unrelated toxicity was not associated with dose (P = 0.0920 for grade ≥ 3, P = 0.4194 for grade ≥ 1), whereas related toxicity increased with dose (P < 0.0001, both grade ≥ 3 and ≥ 1). Similar results were observed across toxicity categories. In the five-tier system, toxicities attributed as "possibly," "probably," or "definitely" related were associated with dose (all P < 0.0001), whereas toxicities attributed as "unlikely" or "unrelated" were not (all P > 0.1).
Reassuringly, we did not observe an association between unrelated toxicity rate and dose, an association that could only have been explained by physician misattribution. Our findings also confirmed our expectation that related toxicity rate increases with dose. Our analysis supports simplifying attribution to a two-tier system by collapsing "possibly," "probably," and "definitely" related.
I期研究依赖研究人员准确判定不良事件与研究药物相关或不相关。该信息最终用于帮助确定安全剂量。I期环境中的判定尚未得到广泛研究,且由于缺乏金标准,评估判定的准确性变得复杂。我们检查了剂量-毒性关系作为判定和毒性类别的函数,以评估毒性误判的证据。
使用了2000年至2010年启动的38项I期研究的个体患者记录。剂量定义为每项研究中给予的最大剂量的百分比。通过图形和逻辑回归探索剂量与患者水平毒性之间的关系。所有P值均为双侧。
分析了来自1156名患者的11909例毒性事件。不相关毒性与剂量无关(≥3级时P = 0.0920,≥1级时P = 0.4194),而相关毒性随剂量增加(≥3级和≥1级时P均<0.0001)。在不同毒性类别中观察到类似结果。在五级系统中,归因于“可能”、“很可能”或“肯定”相关的毒性与剂量相关(所有P < 0.0001),而归因于“不太可能”或“不相关”的毒性则不然(所有P > 0.1)。
令人放心的是,我们未观察到不相关毒性率与剂量之间的关联,这种关联只能由医生误判来解释。我们的研究结果也证实了我们的预期,即相关毒性率随剂量增加。我们的分析支持通过合并“可能”、“很可能”和“肯定”相关来将判定简化为两级系统。