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Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare
INTRODUCTION: In retrospective studies, the effect of a given intervention is usually evaluated by using statistical tests to compare data from before and after the intervention. A problem with this approach is that the presence of underlying trends can lead to incorrect conclusions. This study aime...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362493/ https://www.ncbi.nlm.nih.gov/pubmed/30805023 http://dx.doi.org/10.1155/2019/9810675 |
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author | Valsamis, Epaminondas Markos Ricketts, David Husband, Henry Rogers, Benedict Aristotle |
author_facet | Valsamis, Epaminondas Markos Ricketts, David Husband, Henry Rogers, Benedict Aristotle |
author_sort | Valsamis, Epaminondas Markos |
collection | PubMed |
description | INTRODUCTION: In retrospective studies, the effect of a given intervention is usually evaluated by using statistical tests to compare data from before and after the intervention. A problem with this approach is that the presence of underlying trends can lead to incorrect conclusions. This study aimed to develop a rigorous mathematical method to analyse temporal variation and overcome these limitations. METHODS: We evaluated hip fracture outcomes (time to surgery, length of stay, and mortality) from a total of 2777 patients between April 2011 and September 2016, before and after the introduction of a dedicated hip fracture unit (HFU). We developed a novel modelling method that fits progressively more complex linear sections to the time series using least squares regression. The method was used to model the periods before implementation, after implementation, and of the whole study period, comparing goodness of fit using F-tests. RESULTS: The proposed method offered reliable descriptions of the temporal evolution of the time series and augmented conclusions that were reached by mere group comparisons. Reductions in time to surgery, length of stay, and mortality rates that group comparisons would have credited to the hip fracture unit appeared to be due to unrelated underlying trends. CONCLUSION: Temporal analysis using segmented linear regression models can reveal secular trends and is a valuable tool to evaluate interventions in retrospective studies. |
format | Online Article Text |
id | pubmed-6362493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-63624932019-02-25 Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare Valsamis, Epaminondas Markos Ricketts, David Husband, Henry Rogers, Benedict Aristotle Comput Math Methods Med Research Article INTRODUCTION: In retrospective studies, the effect of a given intervention is usually evaluated by using statistical tests to compare data from before and after the intervention. A problem with this approach is that the presence of underlying trends can lead to incorrect conclusions. This study aimed to develop a rigorous mathematical method to analyse temporal variation and overcome these limitations. METHODS: We evaluated hip fracture outcomes (time to surgery, length of stay, and mortality) from a total of 2777 patients between April 2011 and September 2016, before and after the introduction of a dedicated hip fracture unit (HFU). We developed a novel modelling method that fits progressively more complex linear sections to the time series using least squares regression. The method was used to model the periods before implementation, after implementation, and of the whole study period, comparing goodness of fit using F-tests. RESULTS: The proposed method offered reliable descriptions of the temporal evolution of the time series and augmented conclusions that were reached by mere group comparisons. Reductions in time to surgery, length of stay, and mortality rates that group comparisons would have credited to the hip fracture unit appeared to be due to unrelated underlying trends. CONCLUSION: Temporal analysis using segmented linear regression models can reveal secular trends and is a valuable tool to evaluate interventions in retrospective studies. Hindawi 2019-01-22 /pmc/articles/PMC6362493/ /pubmed/30805023 http://dx.doi.org/10.1155/2019/9810675 Text en Copyright © 2019 Epaminondas Markos Valsamis et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Valsamis, Epaminondas Markos Ricketts, David Husband, Henry Rogers, Benedict Aristotle Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare |
title | Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare |
title_full | Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare |
title_fullStr | Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare |
title_full_unstemmed | Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare |
title_short | Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare |
title_sort | segmented linear regression models for assessing change in retrospective studies in healthcare |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362493/ https://www.ncbi.nlm.nih.gov/pubmed/30805023 http://dx.doi.org/10.1155/2019/9810675 |
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