Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

https://doi.org/10.1016/j.jpsychores.2020.110211Get rights and content
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Highlights

  • Intensive longitudinal time-series data can identify personalized treatment targets.

  • Twelve research labs varied in how they analyzed one patient's time-series data.

  • Moreover, they varied widely in selected treatment targets and the underlying rationale.

  • Results of person-specific analyses are currently conditional on subjective choices.

Abstract

Objective

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.

Methods

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.

Results

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.

Conclusion

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.

Keywords

Time-series analysis
Electronic diary
Personalized medicine
Mental disorders
Psychological networks
Crowdsourcing science

Cited by (0)

This project was initiated by the iLab of the Department of Psychiatry, University Medical Center Groningen, Groningen, the Netherlands (http://ilab-psychiatry.nl). Researchers were funded by a variety of sources, none of which had a role in the design of the study, data collection, analysis, or interpretation of data, nor in writing the manuscript. A. G. C. Wright: National Institute of Mental Health (L30 MH101760); E. Ceulemans and P. Kuppens: KU Leuven Research Council grant (GOA/15/003) and Fund for Scientific Research-Flanders grant (FWO G074319N, G066316N); F. J. Blaauw: The Netherlands Initiative for Education Research (NRO) grant (no.644405–16-401); H. Riese and M. Wichers: Innovatiefonds De Friesland (grant no. DS81); J. A. Bastiaansen, M. N. Servaas and H. Riese: charitable foundation Stichting tot Steun VCVGZ (grant no. 239); L. F. Bringmann: Netherlands Organization for Scientific Research Veni Grant (NWO-Veni 191G.037); M. Wichers: European Research Council (ERC) under the European Union's Horizon 2020 research and innovative programme (ERC-CoG-2015; No. 68146); O. Ryan: Netherlands Organization for Scientific Research Talent Grant (NWO Onderzoekstalent 406–15-128); P. K. Wood: National Institute on Alcohol Abuse and Alcoholism (AA024133); T. J. Trull: National Institute on Alcohol Abuse and Alcoholism (AA024133; AA019546); S.-M. Chow: National Institutes of Health (NIH U24AA027684) and National Science Foundation (NSF IGE-1806874).