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http://bura.brunel.ac.uk/handle/2438/28126Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Filipow, N | - |
| dc.contributor.author | Main, E | - |
| dc.contributor.author | Tanriver, G | - |
| dc.contributor.author | Raywood, E | - |
| dc.contributor.author | Davies, G | - |
| dc.contributor.author | Douglas, H | - |
| dc.contributor.author | Laverty, A | - |
| dc.contributor.author | Stanojevic, S | - |
| dc.date.accessioned | 2024-01-30T16:27:40Z | - |
| dc.date.available | 2024-01-30T16:27:40Z | - |
| dc.date.issued | 2023-05-11 | - |
| dc.identifier | ORCiD ID: Nicole Filipow https://orcid.org/0000-0003-3544-6136 | - |
| dc.identifier | ORCiD ID: Helen Douglas https://orcid.org/0000-0002-5184-6300 | - |
| dc.identifier.citation | Filipow, N. et al. (2023). 'Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies', BMC Medical Research Methodology, 23 (114), pp. 1 - 9. doi: 10.1186/s12874-023-01942-4. | en_US |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28126 | - |
| dc.description | Data Availability: The data that support the findings of this study are available from Great Ormond Street Hospital, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Great Ormond Street Hospital. | - |
| dc.description.abstract | Copyright © The Author(s) 2023. Background: Clinical outcomes are normally captured less frequently than data from remote technologies, leaving a disparity in volumes of data from these different sources. To align these data, flexible polynomial regression was investigated to estimate personalised trends for a continuous outcome over time. Methods: Using electronic health records, flexible polynomial regression models inclusive of a 1st up to a 4th order were calculated to predict forced expiratory volume in 1 s (FEV1) over time in children with cystic fibrosis. The model with the lowest AIC for each individual was selected as the best fit. The optimal parameters for using flexible polynomials were investigated by comparing the measured FEV1 values to the values given by the individualised polynomial. Results: There were 8,549 FEV1 measurements from 267 individuals. For individuals with > 15 measurements (n = 178), the polynomial predictions worked well; however, with < 15 measurements (n = 89), the polynomial models were conditional on the number of measurements and time between measurements. The method was validated using BMI in the same population of children. Conclusion: Flexible polynomials can be used to extrapolate clinical outcome measures at frequent time intervals to align with daily data captured through remote technologies. | en_US |
| dc.description.sponsorship | UCL, GOSH and Toronto SickKids studentship. GD is supported by a Future Leaders Fellowship from UK Research & Innovation (UKRI), Grant reference: MR/T041285. All research at Great Ormond Street Hospital NHS Foundation Trust and UCL Great Ormond Street Institute of Child Health is made possible by the NIHR Great Ormond Street Hospital Biomedical Research Centre. | en_US |
| dc.format.medium | Electronic | - |
| dc.publisher | BioMed Central (part of Springer Nature) | en_US |
| dc.rights | Copyright © The Author(s) 2023. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | - |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
| dc.subject | remote patient monitoring | en_US |
| dc.subject | polynomial regression | en_US |
| dc.subject | clinical outcomes | en_US |
| dc.subject | chronic disease | en_US |
| dc.subject | missing data | en_US |
| dc.title | Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | https://doi.org/10.1186/s12874-023-01942-4 | - |
| dc.relation.isPartOf | BMC Medical Research Methodology | - |
| pubs.issue | 1 | - |
| pubs.publication-status | Published | - |
| pubs.volume | 23 | - |
| dc.identifier.eissn | 1471-2288 | - |
| Appears in Collections: | Dept of Health Sciences Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © The Author(s) 2023. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | 2.36 MB | Adobe PDF | View/Open |
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