Performing high-powered studies efficiently with sequential analyses
Performing high-powered studies efficiently with sequential analyses
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DOI：
10.1002/ejsp.2023
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发表时间：
2014-12-01
影响因子：
3.9
通讯作者：
Lakens, Daniel
中科院分区：
文献类型：
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作者：
Lakens, Daniel
运行具有高统计功效的研究，而心理学中的效应量估计通常不准确，这导致了设计实验时的实际挑战。这一挑战可以通过在数据收集仍在进行的同时进行顺序分析来解决。在中期分析时，只要结果足以令人信服地得出存在效应的结论，就可以停止数据收集，可以收集更多数据，或者如果继续收集数据，极不可能观察到预测的效应，就可以终止研究。可以在控制1型错误率的同时进行此类中期分析。序贯分析可以大大提高数据收集的效率。自适应设计提供了额外的灵活性，其中样本量在观察到的效应量的基础上增加。预登记的必要性，防止实验者偏见的方法，以及贝叶斯方法和零假设显著性检验（NHST）之间的比较进行了讨论。序贯分析在大规模医学试验中得到了广泛的应用，为进行高效能的信息实验提供了一种有效的方法。我希望这篇介绍能提供一个实用的入门，让研究人员在他们的研究中纳入序列分析。版权所有（c）2014约翰威利父子有限公司
Running studies with high statistical power, while effect size estimates in psychology are often inaccurate, leads to a practical challenge when designing an experiment. This challenge can be addressed by performing sequential analyses while the data collection is still in progress. At an interim analysis, data collection can be stopped whenever the results are convincing enough to conclude that an effect is present, more data can be collected, or the study can be terminated whenever it is extremely unlikely that the predicted effect will be observed if data collection would be continued. Such interim analyses can be performed while controlling the Type 1 error rate. Sequential analyses can greatly improve the efficiency with which data are collected. Additional flexibility is provided by adaptive designs where sample sizes are increased on the basis of the observed effect size. The need for pre-registration, ways to prevent experimenter bias, and a comparison between Bayesian approaches and null-hypothesis significance testing (NHST) are discussed. Sequential analyses, which are widely used in large-scale medical trials, provide an efficient way to perform high-powered informative experiments. I hope this introduction will provide a practical primer that allows researchers to incorporate sequential analyses in their research. Copyright (c) 2014 John Wiley & Sons, Ltd.