The statistical power of epidemiological studies analyzing the relationship between exposure to ionizing radiation and cancer, with special reference to childhood leukemia and natural background radiation.
The statistical power of epidemiological studies analyzing the relationship between exposure to ionizing radiation and cancer, with special reference to childhood leukemia and natural background radiation.
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DOI：
10.1667/rr2110.1
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发表时间：
2010-09
影响因子：
3.4
通讯作者：
Kendall GM
中科院分区：
文献类型：
--
作者：
Little MP;Wakeford R;Lubin JH;Kendall GM
小议员，韦克福德河，卢宾和肯德尔通用汽车公司，分析电离辐射照射与癌症，特别是儿童白血病和自然本底辐射之间关系的流行病学研究的统计功效。Radiat.儿童白血病的病因仍然是未知的，虽然基于日本原子弹幸存者的风险模型表明，长期暴露于低水平天然本底电离辐射所累积的剂量会大大增加儿童白血病的风险。在本文中，一种新的蒙特卡罗评分检验方法被用来评估队列，生态和病例对照研究设计的统计能力，使用线性低剂量部分的BEIR V模型来自日本的数据。英国儿童白血病随访10年（或20年），约4600（或9200）例，在基于个体的队列设计中，有67.9%（或90.9%）的机会检测到过量（在5%的显著性水平，单侧检验）;风险的极端异质性几乎没有差异。对于生态设计，这些数字减少到57.9%（或83.2%）。每例病例5个对照的病例对照研究达到了队列设计的大部分功效，61.1%（或86.0%）。然而，参与偏倚可能会严重影响需要个人同意的研究，并且基于区域的研究会遇到严重的解释问题。因此，基于登记册的研究，特别是那些利用预测剂量而无需面谈的研究，具有相当大的优势。我们认为，以前的研究一直不够有力（所有的权力<80%），有些也受到不可量化的偏见和混淆。足够大规模的研究应能够探测出可归因于自然本底辐射的预测风险。
Little M.P., Wakeford R., Lubin J.H. and Kendall G.M., The statistical power of epidemiological studies analyzing the relationship between exposure to ionizing radiation and cancer, with special reference to childhood leukemia and natural background radiation. Radiat. Res. The etiology of childhood leukemia remains generally unknown, although risk models based on the Japanese A-bomb survivors imply that the dose accumulated from protracted exposure to low-level natural background ionizing radiation materially raises the risk of leukemia in children. In this paper a novel Monte Carlo score-test methodology is used to assess the statistical power of cohort, ecological and case-control study designs, using the linear low-dose part of the BEIR V model derived from the Japanese data. With 10 (or 20) years of follow-up of childhood leukemias in Great Britain, giving about 4600 (or 9200) cases, under an individual-based cohort design there is 67.9% (or 90.9%) chance of detecting an excess (at 5% significance level, 1-sided test); little difference is made by extreme heterogeneity in risk. For an ecological design these figures reduce to 57.9% (or 83.2%). Case-control studies with five controls per case achieve much of the power of a cohort design, 61.1% (or 86.0%). However, participation bias may seriously affect studies that require individual consent, and area-based studies are subject to severe interpretational problems. For this reason register-based studies, in particular those that make use of predicted doses that avoid the need for interviews, have considerable advantages. We argue that previous studies have been underpowered (all have power <80%), and some are also subject to unquantifiable biases and confounding. Sufficiently large studies should be capable of detecting the predicted risk attributable to natural background radiation.