Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands.
Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
Schizophr Bull. 2018 Oct 17;44(6):1175-1179. doi: 10.1093/schbul/sby118.
Identifying modifiable factors through environmental research may improve mental health outcomes. However, several challenges need to be addressed to optimize the chances of success. By analyzing the Netherlands Mental Health Survey and Incidence Study-2 data, we provide a data-driven illustration of how closely connected the exposures and the mental health outcomes are and how model and variable specifications produce "vibration of effects" (variation of results under multiple different model specifications). Interdependence of exposures is the rule rather than the exception. Therefore, exposure-wide systematic approaches are needed to separate genuine strong signals from selective reporting and dissect sources of heterogeneity. Pre-registration of protocols and analytical plans is still uncommon in environmental research. Different studies often present very different models, including different variables, despite examining the same outcome, even if consistent sets of variables and definitions are available. For datasets that are already collected (and often already analyzed), the exploratory nature of the work should be disclosed. Exploratory analysis should be separated from prospective confirmatory research with truly pre-specified analysis plans. In the era of big-data, where very low P values for trivial effects are detected, several safeguards may be considered to improve inferences, eg, lowering P-value thresholds, prioritizing effect sizes over significance, analyzing pre-specified falsification endpoints, and embracing alternative approaches like false discovery rates and Bayesian methods. Any claims for causality should be cautious and preferably avoided, until intervention effects have been validated. We hope the propositions for amendments presented here may help with meeting these pressing challenges.
通过环境研究识别可改变的因素可能会改善心理健康结果。然而，需要解决几个挑战才能优化成功的机会。通过分析荷兰心理健康调查和发病率研究-2 数据，我们提供了一个数据驱动的说明，展示了暴露因素和心理健康结果之间的紧密联系，以及模型和变量规范如何产生“效果波动”（在多个不同的模型规范下结果的变化）。暴露因素的相互依存是常态，而不是例外。因此，需要采用广泛的系统性方法来区分真实的强信号与选择性报告，并剖析异质性的来源。在环境研究中，协议和分析计划的预先注册仍然不常见。尽管研究的是相同的结果，但不同的研究经常提出非常不同的模型，包括不同的变量，即使有一致的变量集和定义可用。对于已经收集（并且通常已经分析）的数据集，应该披露工作的探索性。探索性分析应与具有真正预先指定分析计划的前瞻性确证性研究分开。在大数据时代，即使是微不足道的效应也能检测到非常低的 P 值，因此可以考虑采取一些措施来改进推论，例如降低 P 值阈值、优先考虑效应大小而不是显著性、分析预先指定的证伪终点，并采用错误发现率和贝叶斯方法等替代方法。任何因果关系的主张都应该谨慎，最好避免，直到干预效果得到验证。我们希望这里提出的修正案建议能够帮助应对这些紧迫的挑战。
