Bastiaansen Jojanneke A, Kunkels Yoram K, Blaauw Frank J, Boker Steven M, Ceulemans Eva, Chen Meng, Chow Sy-Miin, de Jonge Peter, Emerencia Ando C, Epskamp Sacha, Fisher Aaron J, Hamaker Ellen L, Kuppens Peter, Lutz Wolfgang, Meyer M Joseph, Moulder Robert, Oravecz Zita, Riese Harriëtte, Rubel Julian, Ryan Oisín, Servaas Michelle N, Sjobeck Gustav, Snippe Evelien, Trull Timothy J, Tschacher Wolfgang, van der Veen Date C, Wichers Marieke, Wood Phillip K, Woods William C, Wright Aidan G C, Albers Casper J, Bringmann Laura F
Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands; Department of Education and Research, Friesland Mental Health Care Services, Leeuwarden, the Netherlands.
Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands.
J Psychosom Res. 2020 Aug 5;137:110211. doi: 10.1016/j.jpsychores.2020.110211.
One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them.
To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment.
Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely.
This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation.
经验取样法（ESM）的一个前景是，对个体日常生活中的情绪、认知和行为进行统计分析，可用于确定相关治疗靶点。临床应用的一个必要条件是，这种针对个体的时间序列分析结果并非完全依赖于进行分析的研究人员。
为评估这一点，我们将一名个体患者的ESM数据分析众包给12个知名研究团队，询问他们会建议治疗临床医生在后续治疗中针对哪些症状。
在分析的不同阶段，从预处理步骤（如变量选择、聚类、缺失数据处理）到统计类型和选择靶点的基本原理，差异都很明显。大多数团队确实纳入了一种向量自回归模型类型，研究症状随时间的关系。尽管大多数团队相信他们选择的靶点会为临床医生提供有用信息，但没有一个建议是相似的：所选靶点的数量（0 - 16个）和性质差异很大。
本研究表明，基于使用ESM数据的个性化模型选择治疗靶点目前高度依赖于主观分析选择，并突出了在迈向临床应用过程中需要解决的关键概念和方法问题。
