A framework to build similarity-based cohorts for personalized treatment advice: a standardized, but flexible workflow with the R package SimBaCo
Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
May 29, 2020
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| In: |
PLOS ONE
Year: 2020, Jahrgang: 15, Heft: 5 |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0233686 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1371/journal.pone.0233686 Verlag, lizenzpflichtig, Volltext: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233686 
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| Verfasserangaben: | Lucas Wirbka, Walter E. Haefeli, Andreas D. Meid |
MARC
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| 520 | |a Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called personalized analyses based on a data subset whose observations resemble a particular patient for whom a decision is to be made. Claims data from statutory health insurance companies could provide necessary information for such personalized analyses. To derive treatment recommendations from them for a particular patient in everyday care, an automated, reproducible and efficiently programmed workflow would be required. We introduce the R-package SimBaCo (Similarity-Based Cohort generation) offering a simple, but modular, and intuitive framework for this task. With the six built-in R-functions, this framework allows the user to create similarity cohorts tailored to the characteristics of particular patients. An exemplary workflow illustrates the distinct steps beginning with an initial cohort selection according to inclusion and exclusion criteria. A plotting function facilitates investigating a particular patient’s characteristics relative to their distribution in a reference cohort, for example the initial cohort or the precision cohort after the data has been trimmed in accordance with chosen variables for similarity finding. Such precision cohorts allow any form of personalized analysis, for example personalized analyses of comparative effectiveness or customized prediction models developed from precision cohorts. In our exemplary workflow, we provide such a treatment comparison whereupon a treatment decision for a particular patient could be made. This is only one field of application where personalized results can directly support the process of clinical reasoning by leveraging information from individual patient data. With this modular package at hand, personalized studies can efficiently weight benefits and risks of treatment options of particular patients. | ||
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