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Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare
INTRODUCTION: In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguishing change due to secular trends from change due to an intervention. Interrupted time-series analysis has been sh...
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/PMC6925779/ https://www.ncbi.nlm.nih.gov/pubmed/31885678 http://dx.doi.org/10.1155/2019/3478598 |
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author | Valsamis, Epaminondas Markos Husband, Henry Chan, Gareth Ka-Wai |
author_facet | Valsamis, Epaminondas Markos Husband, Henry Chan, Gareth Ka-Wai |
author_sort | Valsamis, Epaminondas Markos |
collection | PubMed |
description | INTRODUCTION: In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguishing change due to secular trends from change due to an intervention. Interrupted time-series analysis has been shown to be effective in describing trends in retrospective time-series and in detecting change, but methods are often biased towards the point of the intervention. Binary outcomes are typically modelled by logistic regression where the log-odds of the binary event is expressed as a function of covariates such as time, making model parameters difficult to interpret. The aim of this study was to present a technique that directly models the probability of binary events to describe change patterns using linear sections. METHODS: We describe a modelling method that fits progressively more complex linear sections to the time-series of binary variables. Model fitting uses maximum likelihood optimisation and models are compared for goodness of fit using Akaike's Information Criterion. The best model describes the most likely change scenario. We applied this modelling technique to evaluate hip fracture patient mortality rate for a total of 2777 patients over a 6-year period, before and after the introduction of a dedicated hip fracture unit (HFU) at a Level 1, Major Trauma Centre. RESULTS: The proposed modelling technique revealed time-dependent trends that explained how the implementation of the HFU influenced mortality rate in patients sustaining proximal femoral fragility fractures. The technique allowed modelling of the entire time-series without bias to the point of intervention. Modelling the binary variable of interest directly, as opposed to a transformed variable, improved the interpretability of the results. CONCLUSION: The proposed segmented linear regression modelling technique using maximum likelihood estimation can be employed to effectively detect trends in time-series of binary variables in retrospective studies. |
format | Online Article Text |
id | pubmed-6925779 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-69257792019-12-29 Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare Valsamis, Epaminondas Markos Husband, Henry Chan, Gareth Ka-Wai Comput Math Methods Med Research Article INTRODUCTION: In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguishing change due to secular trends from change due to an intervention. Interrupted time-series analysis has been shown to be effective in describing trends in retrospective time-series and in detecting change, but methods are often biased towards the point of the intervention. Binary outcomes are typically modelled by logistic regression where the log-odds of the binary event is expressed as a function of covariates such as time, making model parameters difficult to interpret. The aim of this study was to present a technique that directly models the probability of binary events to describe change patterns using linear sections. METHODS: We describe a modelling method that fits progressively more complex linear sections to the time-series of binary variables. Model fitting uses maximum likelihood optimisation and models are compared for goodness of fit using Akaike's Information Criterion. The best model describes the most likely change scenario. We applied this modelling technique to evaluate hip fracture patient mortality rate for a total of 2777 patients over a 6-year period, before and after the introduction of a dedicated hip fracture unit (HFU) at a Level 1, Major Trauma Centre. RESULTS: The proposed modelling technique revealed time-dependent trends that explained how the implementation of the HFU influenced mortality rate in patients sustaining proximal femoral fragility fractures. The technique allowed modelling of the entire time-series without bias to the point of intervention. Modelling the binary variable of interest directly, as opposed to a transformed variable, improved the interpretability of the results. CONCLUSION: The proposed segmented linear regression modelling technique using maximum likelihood estimation can be employed to effectively detect trends in time-series of binary variables in retrospective studies. Hindawi 2019-12-06 /pmc/articles/PMC6925779/ /pubmed/31885678 http://dx.doi.org/10.1155/2019/3478598 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 Husband, Henry Chan, Gareth Ka-Wai Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare |
title | Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare |
title_full | Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare |
title_fullStr | Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare |
title_full_unstemmed | Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare |
title_short | Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare |
title_sort | segmented linear regression modelling of time-series of binary variables in healthcare |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925779/ https://www.ncbi.nlm.nih.gov/pubmed/31885678 http://dx.doi.org/10.1155/2019/3478598 |
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