Power analysis for generalized linear mixed models in ecology and evolution
Power analysis for generalized linear mixed models in ecology and evolution
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DOI：
10.1111/2041-210x.12306
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发表时间：
2015-02-01
影响因子：
6.6
通讯作者：
Mueller, Pie
中科院分区：
文献类型：
--
作者：
Johnson, Paul C. D.;Barry, Sarah J. E.;Mueller, Pie
我的研究会回答我的研究问题吗？是研究者在设计研究时所能提出的最基本的问题，然而，当用统计学术语来表述时，我的研究的力量是什么？'或我的参数估计值有多精确？- -生态学和进化（EE）领域很少有研究人员试图回答这个问题，尽管研究力度不足或过度会带来有害后果。我们认为，这种不情愿在很大程度上是由于简单的功率分析方法（广义上定义为任何试图量化前瞻性的信息量的研究）的EE研究中常用的复杂模型的不适合。为了鼓励使用功率分析，我们提出了模拟广义线性混合模型（GLSTO）作为一种灵活和方便的方法，功率分析，可以考虑随机效应，过度分散和不同的响应分布。我们说明了基于模拟的功率分析在两个研究方案的好处：估计调查的精度，估计蜱虫负担松鸡和估计的权力，比较疟疾防治的驱虫蚊帐的功效试验。我们提供了一个免费的R函数，sim.glmm，用于从GLCNET进行模拟。模拟数据的分析表明，占现实水平的随机效应和过度分散的功效和精度估计的影响是巨大的，相应的严重影响的形式高达五倍的抽样工作增加的研究设计。我们还显示了实用的模拟识别的情况下，GLMM拟合方法可以执行不佳。这些结果说明了标准的分析功率分析方法的不足和基于仿真的功率分析的灵活性GLSTIC。这些方法的广泛使用应有助于提高EE研究设计的质量。
Will my study answer my research question?' is the most fundamental question a researcher can ask when designing a study, yet when phrased in statistical terms - What is the power of my study?' or How precise will my parameter estimate be?' - few researchers in ecology and evolution (EE) try to answer it, despite the detrimental consequences of performing under- or over-powered research. We suggest that this reluctance is due in large part to the unsuitability of simple methods of power analysis (broadly defined as any attempt to quantify prospectively the informativeness' of a study) for the complex models commonly used in EE research. With the aim of encouraging the use of power analysis, we present simulation from generalized linear mixed models (GLMMs) as a flexible and accessible approach to power analysis that can account for random effects, overdispersion and diverse response distributions. We illustrate the benefits of simulation-based power analysis in two research scenarios: estimating the precision of a survey to estimate tick burdens on grouse chicks and estimating the power of a trial to compare the efficacy of insecticide-treated nets in malaria mosquito control. We provide a freely available R function, sim.glmm, for simulating from GLMMs. Analysis of simulated data revealed that the effects of accounting for realistic levels of random effects and overdispersion on power and precision estimates were substantial, with correspondingly severe implications for study design in the form of up to fivefold increases in sampling effort. We also show the utility of simulations for identifying scenarios where GLMM-fitting methods can perform poorly. These results illustrate the inadequacy of standard analytical power analysis methods and the flexibility of simulation-based power analysis for GLMMs. The wider use of these methods should contribute to improving the quality of study design in EE.