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Frankot MA, Young ME, Haar CV. Understanding Individual Subject Differences through Large Behavioral Datasets: Analytical and Statistical Considerations. Perspect Behav Sci 2024; 47:225-250. [PMID: 38660505 PMCID: PMC11035513 DOI: 10.1007/s40614-023-00388-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2023] [Indexed: 04/26/2024] Open
Abstract
A core feature of behavior analysis is the single-subject design, in which each subject serves as its own control. This approach is powerful for identifying manipulations that are causal to behavioral changes but often fails to account for individual differences, particularly when coupled with a small sample size. It is more common for other subfields of psychology to use larger-N approaches; however, these designs also often fail to account for the individual by focusing on aggregate-level data only. Moving forward, it is important to study individual differences to identify subgroups of the population that may respond differently to interventions and to improve the generalizability and reproducibility of behavioral science. We propose that large-N datasets should be used in behavior analysis to better understand individual subject variability. First, we describe how individual differences have been historically treated and then outline practical reasons to study individual subject variability. Then, we describe various methods for analyzing large-N datasets while accounting for the individual, including correlational analyses, machine learning, mixed-effects models, clustering, and simulation. We provide relevant examples of these techniques from published behavioral literature and from a publicly available dataset compiled from five different rat experiments, which illustrates both group-level effects and heterogeneity across individual subjects. We encourage other behavior analysts to make use of the substantial advancements in online data sharing to compile large-N datasets and use statistical approaches to explore individual differences.
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Affiliation(s)
- Michelle A. Frankot
- Injury and Recovery Laboratory, Department of Psychology, West Virginia University, Morgantown, WV USA
- Injury and Recovery Laboratory, Department of Neuroscience, Ohio State University, 460 West 12th Avenue, Columbus, OH 43210 USA
| | - Michael E. Young
- Department of Psychological Sciences, Kansas State University, Manhattan, KS USA
| | - Cole Vonder Haar
- Injury and Recovery Laboratory, Department of Psychology, West Virginia University, Morgantown, WV USA
- Injury and Recovery Laboratory, Department of Neuroscience, Ohio State University, 460 West 12th Avenue, Columbus, OH 43210 USA
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Coorey G, Figtree GA, Fletcher DF, Snelson VJ, Vernon ST, Winlaw D, Grieve SM, McEwan A, Yang JYH, Qian P, O'Brien K, Orchard J, Kim J, Patel S, Redfern J. The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field. NPJ Digit Med 2022; 5:126. [PMID: 36028526 PMCID: PMC9418270 DOI: 10.1038/s41746-022-00640-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.
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Affiliation(s)
- Genevieve Coorey
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia. .,The George Institute for Global Health, Sydney, NSW, Australia.
| | - Gemma A Figtree
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David F Fletcher
- University of Sydney, School of Chemical and Biomolecular Engineering, Sydney, NSW, Australia
| | - Victoria J Snelson
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Stephen Thomas Vernon
- Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia.,Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David Winlaw
- Cincinnati Children's Hospital Medical Cente, Cincinnati, OH, USA
| | - Stuart M Grieve
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Alistair McEwan
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia
| | - Jean Yee Hwa Yang
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Pierre Qian
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Westmead Applied Research Centre, Westmead Hospital, Sydney, NSW, Australia
| | - Kieran O'Brien
- Siemens Healthcare Pty Ltd; and Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Jessica Orchard
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Jinman Kim
- University of Sydney, School of Computer Science, Sydney, NSW, Australia
| | - Sanjay Patel
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Royal Prince Alfred Hospital, Sydney, NSW, Australia.,Heart Research Institute, Sydney, NSW, Australia
| | - Julie Redfern
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia
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