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  • Editorial: Creating Evidence From Real World Patient Digital Data

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    McDonald528341-Published.pdf (544.7Kb)
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    Author(s)
    Nikles, J
    Daza, EJ
    McDonald, S
    Hekler, E
    Schork, NJ
    Griffith University Author(s)
    McDonald, Suzanne
    Year published
    2021
    Metadata
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    Abstract
    Over the last 5 years there has been a tremendous rise in interest in personalized medicine and patient-centred healthcare. This has resulted in an expanding focus on the value of single-case research methods (also known as N-of-1 or Single-Case Designs (SCDs)). SCDs use repeated measures, frequent data collection and patient-reported outcome measures to draw conclusions about an individual. We use “SCD” as a broad term that includes all SCD sub-designs, encompassing Single-Case Experimental Designs (SCEDs), N-of-1 randomised controlled trials (N-of-1 trials) and Single-Case Observational Designs (SCODs)—all with specific ...
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    Over the last 5 years there has been a tremendous rise in interest in personalized medicine and patient-centred healthcare. This has resulted in an expanding focus on the value of single-case research methods (also known as N-of-1 or Single-Case Designs (SCDs)). SCDs use repeated measures, frequent data collection and patient-reported outcome measures to draw conclusions about an individual. We use “SCD” as a broad term that includes all SCD sub-designs, encompassing Single-Case Experimental Designs (SCEDs), N-of-1 randomised controlled trials (N-of-1 trials) and Single-Case Observational Designs (SCODs)—all with specific features (see Box 1).
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    Journal Title
    Frontiers in Computer Science
    Volume
    2
    DOI
    https://doi.org/10.3389/fcomp.2020.636996
    Copyright Statement
    © 2021 Nikles, Daza, McDonald, Hekler and Schork. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
    Subject
    Data management and data science
    Publication URI
    http://hdl.handle.net/10072/411520
    Collection
    • Journal articles

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