ArticleComputational and mathematical methods in medicine2019
Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare.
Article in Computational and mathematical methods in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Fault detection for Li-ion batteries of electric vehicles with segmented regression method.Scientific reports · 2024Article
- Impact of the mental health law revision restricting hospitalization on healthcare utilization in South Korea using interrupted time series analysis.Scientific reports · 2024Article
- Impacts of bariatric surgery in health outcomes and health care costs in Brazil: Interrupted time series analysis of multi-panel data.BMC health services research · 2022Article
- Financial time series forecasting using optimized multistage wavelet regression approach.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2022Article
- Social Contacts and Transmission of COVID-19 in British Columbia, Canada.Frontiers in public health · 2022Article
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3 authors.
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Abstract
introductionIn 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.
methodsWe 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.
resultsThe 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.
conclusionThe 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.
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