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首页> 美国卫生研究院文献>Ecology and Evolution >The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms
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The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms

机译：统计工具在转基因植物对非目标生物的推定效果的现场测试中的使用

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To fulfill existing guidelines, applicants that aim to place their genetically modified (GM) insect-resistant crop plants on the market are required to provide data from field experiments that address the potential impacts of the GM plants on nontarget organisms (NTO's). Such data may be based on varied experimental designs. The recent EFSA guidance document for environmental risk assessment (2010) does not provide clear and structured suggestions that address the statistics of field trials on effects on NTO's. This review examines existing practices in GM plant field testing such as the way of randomization, replication, and pseudoreplication. Emphasis is placed on the importance of design features used for the field trials in which effects on NTO's are assessed. The importance of statistical power and the positive and negative aspects of various statistical models are discussed. Equivalence and difference testing are compared, and the importance of checking the distribution of experimental data is stressed to decide on the selection of the proper statistical model. While for continuous data (e.g., pH and temperature) classical statistical approaches – for example, analysis of variance (ANOVA) – are appropriate, for discontinuous data (counts) only generalized linear models (GLM) are shown to be efficient. There is no golden rule as to which statistical test is the most appropriate for any experimental situation. In particular, in experiments in which block designs are used and covariates play a role GLMs should be used. Generic advice is offered that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in this testing. The combination of decision trees and a checklist for field trials, which are provided, will help in the interpretation of the statistical analyses of field trials and to assess whether such analyses were correctly applied.We offer generic advice to risk assessors and applicants that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in field testing.
机译：为了满足现有准则，要求将其转基因（GM）抗虫农作物投放市场的申请人必须提供来自田间实验的数据，以解决转基因植物对非靶标生物（NTO）的潜在影响。这样的数据可以基于各种实验设计。 EFSA最近的环境风险评估指导文件（2010年）并未提供明确，结构化的建议，无法解决对NTO产生影响的现场试验统计数据。本文回顾了转基因植物现场测试中的现有做法，例如随机化，复制和伪复制的方式。重点放在用于实地试验的设计功能的重要性上，其中评估了对NTO的影响。讨论了统计能力的重要性以及各种统计模型的正面和负面方面。比较了等效性和差异测试，强调了检查实验数据分布的重要性，以决定选择正确的统计模型。虽然对于连续数据（例如pH和温度），经典的统计方法（例如，方差分析（ANOVA））是合适的，但对于不连续的数据（计数），仅通用线性模型（GLM）被证明是有效的。关于哪种统计检验最适合任何实验情况，没有黄金定律。特别地，在使用块设计并且协变量起作用的实验中，应使用GLM。提供的一般建议将有助于现场测试的建立以及对该测试中获得的数据的解释和数据分析。提供的决策树和现场试验检查表的组合将有助于解释现场试验的统计分析，并评估此类分析是否正确应用。我们为风险评估者和申请人提供通用建议在现场测试的建立以及对现场测试中获得的数据的解释和数据分析方面。

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