Chen J J, Lin K K, Huque M, Arani R B
Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, USA.
Biometrics. 2000 Jun;56(2):586-92. doi: 10.1111/j.0006-341x.2000.00586.x.
A typical animal carcinogenicity experiment routinely analyzes approximately 10-30 tumor sites. Comparisons of tumor responses between dosed and control groups and dose-related trend tests are often evaluated for each individual tumor site/type separately. p-Value adjustment approaches have been proposed for controlling the overall Type I error rate or familywise error rate (FWE). However, these adjustments often result in reducing the power to detect a dose effect. This paper proposes using weighted adjustments by assuming that each tumor can be classified as either class A or class B based on prior considerations. The tumors in class A, which are considered as more critical endpoints, are given less adjustment. Two weighted methods of adjustments are presented, the weighted p adjustment and weighted alpha adjustment. A Monte Carlo simulation shows that both weighted adjustments control the FWE well. Furthermore, the power increases if a treatment-dependent tumor is analyzed as in class A tumors and the power decreases if it is analyzed as in class B tumors. A data set from a National Toxicology Program (NTP) 2-year animal carcinogenicity experiment with 13 tumor types/sites observed in male mice was analyzed using the proposed methods. The modified poly-3 test was used to test for increased carcinogenicity since it has been adopted by the NTP as a standard test for a dose-related trend. The unweighted adjustment analysis concluded that there was no statistically significant dose-related trend. Using the Food and Drug Administration classification scheme for the weighted adjustment analyses, two rare tumors (with background rates of 1% or less) were analyzed as class A tumors and 11 common tumors (with background rates higher than 1%) as class B. Both weighted analyses showed a significant dose-related trend for one rare tumor.
典型的动物致癌性实验通常会对大约10 - 30个肿瘤部位进行常规分析。给药组和对照组之间的肿瘤反应比较以及剂量相关趋势检验通常会针对每个单独的肿瘤部位/类型分别进行评估。已经提出了p值调整方法来控制总体I型错误率或家族性错误率（FWE）。然而，这些调整往往会降低检测剂量效应的效能。本文提出通过假设每个肿瘤可以根据先前的考虑分为A类或B类来使用加权调整。被视为更关键终点的A类肿瘤给予较少的调整。本文提出了两种加权调整方法，加权p调整和加权α调整。蒙特卡罗模拟表明，两种加权调整都能很好地控制FWE。此外，如果将依赖于治疗的肿瘤按照A类肿瘤进行分析，效能会增加；如果按照B类肿瘤进行分析，效能则会降低。使用所提出的方法对来自国家毒理学计划（NTP）的一项为期两年的动物致癌性实验的数据集进行了分析，该实验在雄性小鼠中观察到了13种肿瘤类型/部位。由于已被NTP采用作为剂量相关趋势的标准检验，因此使用改良的多聚-3检验来检验致癌性增加情况。未加权调整分析得出没有统计学上显著的剂量相关趋势。使用美国食品药品监督管理局的分类方案进行加权调整分析时，将两种罕见肿瘤（背景发生率为1%或更低）作为A类肿瘤进行分析，将11种常见肿瘤（背景发生率高于1%）作为B类肿瘤进行分析。两种加权分析均显示一种罕见肿瘤存在显著的剂量相关趋势。
