论文标题
技术使用和青少年福祉的规范分析：统计有效性和贝叶斯提案
Specification analysis for technology use and teenager well-being: statistical validity and a Bayesian proposal
论文作者
论文摘要
科学的一个关键问题是评估数据分析选择的鲁棒性，同时避免选择性报告并提供有效的推论。规范曲线分析是一种旨在防止选择性报告的工具。 las，当用于推理时，由于错误调整了协变量，并且会产生严重的偏见和假阳性，并且掩盖了重要的治疗效果异质性。作为我们激励的应用，它导致一项有影响力的研究得出结论，技术使用与青少年心理健康之间没有相关的关联。我们讨论这些问题，并提出有效推理的策略。贝叶斯规格曲线分析（BSCA）使用贝叶斯模型平均纳入跨处理，结果和亚群的协变量和异质效应。 BSCA对青少年幸福感有很大不同的见解，表明与技术的联系因设备，性别和评估福祉（青少年或其父母）而异。
A key issue in science is assessing robustness to data analysis choices, while avoiding selective reporting and providing valid inference. Specification Curve Analysis is a tool intended to prevent selective reporting. Alas, when used for inference it can create severe biases and false positives, due to wrongly adjusting for covariates, and mask important treatment effect heterogeneity. As our motivating application, it led an influential study to conclude there is no relevant association between technology use and teenager mental well-being. We discuss these issues and propose a strategy for valid inference. Bayesian Specification Curve Analysis (BSCA) uses Bayesian Model Averaging to incorporate covariates and heterogeneous effects across treatments, outcomes and sub-populations. BSCA gives significantly different insights into teenager well-being, revealing that the association with technology differs by device, gender and who assesses well-being (teenagers or their parents).
