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首页> 外文期刊>Journal of dairy science >Invited Review Integrating Quantitative Findings from Multiple Studies Using Mixed Model Methodology
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Invited Review Integrating Quantitative Findings from Multiple Studies Using Mixed Model Methodology

机译：使用混合模型方法将多个研究的定量发现整合在一起的特邀评论

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In animal agriculture, the need to understand com- plex biological, environmental, and management rela- tionships is increasing. In addition, as knowledge in- creases and profit margins shrink, our ability and desire to predict responses to various management decisions also increases. Therefore, the purpose of this review is to help show how improved mathematical and statisti- cal tools and computer technology can help us gain more accurate information from published studies and im- prove future research. Researchers, in several recent reviews, have gathered data from multiple published studies and attempted to formulate a quantitative model that best explains the observations. In statistics, this process has been labeled meta-analysis. Generally, there are large differences between studies: e. g., differ- ent physiological status of the experimental units, dif ferent experimental design, different measurement methods, and laboratory technicians. From a statistical standpoint, studies are blocks and their effects must be considered random because the inference being sought is to future, unknown studies. Meta-analyses in the animal sciences have generally ignored the Study effect. Because data gathered across studies are unbal- anced with respect to predictor variables, ignoring the Study effect has as a consequence that the estimation of parameters (slopes and intercept) of regression mod- els can be severely biased. Additionally, variance esti- mates are biased upward, resulting in large type II errors when testing the effect of independent variables. Historically, the Study effect has been considered a fixed effect not because of a strong argument that such effect is indeed fixed but because of our prior inability to efficiently solve even modest-sized mixed models (those containing both fixed and random effects). Modern sta- tistical software has, however, overcome this limitation. Consequently, meta-analyses should now incorporate the Study effect and its interaction effects as random components of a mixed model. This would result in better prediction equations of biological systems and a more accurate description of their prediction errors.
机译：在动物农业中，越来越需要了解复杂的生物，环境和管理关系。此外，随着知识的增加和利润率的下降，我们预测各种管理决策响应的能力和愿望也随之增加。因此，本综述的目的是帮助说明改进的数学和统计学工具以及计算机技术如何帮助我们从已发表的研究中获得更准确的信息并改善未来的研究。研究人员在最近的几次评论中，从多项已发表的研究中收集了数据，并试图建立一个最能解释观察结果的定量模型。在统计数据中，该过程被称为荟萃分析。通常，研究之间存在很大差异：e。例如，实验单元的生理状态不同，实验设计不同，测量方法和实验室技术人员也不同。从统计学的角度来看，研究是障碍，其影响必须被认为是随机的，因为所寻求的推论是对未来未知的研究。动物科学中的荟萃分析通常忽略了研究效果。由于跨研究收集的数据在预测变量方面是不平衡的，因此忽略研究效果会导致回归模型的参数（斜率和截距）估计严重偏差。另外，方差估计向上偏，在测试自变量的影响时会导致较大的II型误差。从历史上看，研究效应被认为是固定效应，不是因为有力的论据确实是固定的，而是因为我们先前无法有效地解决甚至中等大小的混合模型（包含固定效应和随机效应的模型）。但是，现代的统计软件已经克服了这一限制。因此，荟萃分析现在应将研究效应及其相互作用效应纳入混合模型的随机组成部分。这将导致更好的生物系统预测方程式以及对其预测误差的更准确描述。

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