Schroeder Jessica, Karkar Ravi, Fogarty James, Kientz Julie A, Munson Sean A, Kay Matthew
University of Washington, Seattle, WA.
University of Michigan, Ann Arbor, MI.
J Healthc Inform Res. 2019 Mar;3(1):124-155. doi: 10.1007/s41666-018-0033-x. Epub 2018 Sep 25.
The rise of affordable sensors and apps has enabled people to monitor various health indicators via self-tracking. This trend encourages self-experimentation, a subset of self-tracking in which a person systematically explores potential causal relationships to try to answer questions about their health. Although recent research has investigated how to support the data collection necessary for self-experiments, less research has considered the best way to analyze data resulting from these self-experiments. Most tools default to using traditional frequentist methods. However, the US Agency for Healthcare Research and Quality recommends using Bayesian analysis for n-of-1 studies, arguing from a statistical perspective. To develop a complementary patient-centered perspective on the potential benefits of Bayesian analysis, this paper describes types of questions people want to answer via self-experimentation, as informed by 1) our experiences engaging with irritable bowel syndrome patients and their healthcare providers and 2) a survey investigating what questions individuals want to answer about their health and wellness. We provide examples of how those questions might be answered using 1) frequentist null hypothesis significance testing, 2) frequentist estimation, and 3) Bayesian estimation and prediction. We then provide design recommendations for analyses and visualizations that could help people answer and interpret such questions. We find the majority of the questions people want to answer with self-tracking data are better answered with Bayesian methods than with frequentist methods. Our results therefore provide patient-centered support for the use of Bayesian analysis for n-of-1 studies.
价格亲民的传感器和应用程序的兴起，使人们能够通过自我追踪来监测各种健康指标。这一趋势鼓励了自我实验，它是自我追踪的一个子集，即一个人系统地探索潜在的因果关系，试图回答有关自身健康的问题。尽管最近的研究探讨了如何支持自我实验所需的数据收集，但较少有研究考虑分析这些自我实验产生的数据的最佳方法。大多数工具默认使用传统的频率论方法。然而，美国医疗保健研究与质量局从统计学角度出发，建议在单病例研究中使用贝叶斯分析。为了从以患者为中心的角度补充探讨贝叶斯分析的潜在益处，本文描述了人们希望通过自我实验来回答的问题类型，这些问题的依据是：1）我们与肠易激综合征患者及其医疗服务提供者接触的经验，以及2）一项关于个人希望回答哪些有关自身健康和幸福问题的调查。我们提供了一些示例，说明如何使用1）频率论的零假设显著性检验、2）频率论估计以及3）贝叶斯估计和预测来回答这些问题。然后，我们针对有助于人们回答和解释此类问题的分析及可视化提供设计建议。我们发现，对于人们希望用自我追踪数据回答的大多数问题，使用贝叶斯方法比使用频率论方法能得到更好的答案。因此，我们的研究结果为在单病例研究中使用贝叶斯分析提供了以患者为中心的支持。
