Hypothesis testing, statistical power, and confidence limits in the presence of epistemic uncertainty.
Hypothesis testing, statistical power, and confidence limits in the presence of epistemic uncertainty.
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存在认知不确定性时的假设检验、统计功效和置信限度。
DOI：
10.1097/01.hp.0000270272.98824.66
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发表时间：
2007
期刊：
影响因子：
2.2
通讯作者：
Kopecky,KennethJ
中科院分区：
文献类型：
--
作者：
Stram,DanielO;Thomas,DuncanC;Kopecky,KennethJ
我们写的这篇文章是关于 Eduard Hofer 在 2007 年 3 月号《健康物理学》上发表的一篇文章（Hofer 2007），该文章涉及当剂量估计应用于流行病学研究时处理辐射剂量测定中的主观不确定性的有趣且重要的主题，特别是当为流行病学应用提供一系列替代剂量估计而不是单个“最佳剂量估计”时。我们已经描述了解决这个问题的几种方法（Stram and Kopecky 2003；Kopecky et al. 2004），虽然这些可能无法提供关于这个问题的最终结论，但我们对 Hofer 提出的分析有一个重要的关注，即提出的分析的 I 类错误（或“假阳性”错误）属性。我们的方法（Stram 和 Kopecky 2003）和 Hofer 的方法相似，因为两者都从一个假设开始（通常需要相当大的飞跃）信念）用于估计研究中每个个体的剂量的剂量测定系统可以被视为根据真实剂量的分布提供估计，该分布取决于已知的实际暴露的决定因素。具体来说，Hofer 和我们都假设剂量测定系统根据真实剂量的条件分布为 n 个受试者生成 m 个独立的剂量估计序列（或“重复”）{x i, j}（i= 1,…, n，是研究中个体的索引，j= 1,…, m 表示序列号），给出所有已知的参数、源项、个体数据（不包括结果数据）等，从而确定真实暴露。在我们 2003 年的论文中，我们描述了以类似于所谓的“Berkson 错误”问题（Thomas 等人，1993）的方式处理序列的一些操作特征。具体来说，我们描述了在给定“所有已知”的真实剂量的情况下，使用真实剂量的平均值 {zi} 作为与疾病与暴露相关的线性回归分析中的剂量变量。（我们可以通过对 j 上的 m 个序列 {x i，j} 进行平均来估计 {zi}，假设 m 足够大，以便该平均值的估计非常准确。）因此，例如，只有在标准统计检验（忽略剂量测定误差）得出的结论是，平均剂量 z i 和感兴趣的结果 Y i 之间存在关联，且具有适当的置信度。
We are writing in regard to an article published in the March 2007 issue of Health Physics by Eduard Hofer (Hofer 2007) on the interesting and important subject of dealing with subjective uncertainty in radiation dosimetry when dose estimates are applied to epidemiological studies, especially when a sequence of alternative dose estimates rather than a single “best estimate” of dose is provided for the epidemiological application. We have described several approaches to this problem (Stram and Kopecky 2003; Kopecky et al. 2004), and while these may not provide the last word on this problem we have an important concern regarding the proposed analysis of Hofer, namely the type I error (or “false positive” error) properties of the proposed analysis.Our approach (Stram and Kopecky 2003) and that of Hofer are similar in that both start with an assumption (often requiring a considerable leap of faith) that the dosimetry system used to estimate dose for each individual in the study can be regarded as providing estimates from a distribution of true dose conditional upon what is known about the determinants of the actual exposure. Specifically, both Hofer and we assume that the dosimetry system generates m independent sequences (or “replications”) of dose estimates {x i, j}(i= 1,…, n, is the index for individuals in the study and j= 1,…, m for the sequence number) for the n subjects from the conditional distribution of true dose given all that is known about the parameters, source terms, individual data (excluding outcome data), etc., determining the true exposures. In our 2003 paper, we described some of the operating characteristics of treating the sequences in a manner analogous to what is done in the so called “Berkson error” problem (Thomas et al. 1993). Specifically, we described some statistical implications of using the mean,{z i}, of true dose given “all that is known” about true dose as the dose variable in a linear regression analysis relating disease to exposure.(We may estimate {z i} by averaging the m sequences,{x i, j}, over j, assuming that m is large enough so that the estimation of this mean is very accurate.) Thus, for example, we reject the null hypothesis of “no exposure effect” in this analysis only if standard statistical tests (ignoring dosimetry error) concluded that there was an association between the mean doses, z i, and the outcome of interest, Y i, with the appropriate degree of confidence.
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