Abstract
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.
Highlights
In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention
Using a novel technique for modelling binary variables in retrospective time-series, this study demonstrates the advantage of piecewise linear sections in conveying meaningful information
We employed the presented technique to model change in hip fracture patient outcomes to evaluate the effectiveness of introducing a dedicated hip fracture unit (HFU)
Summary
Change is usually detected by statistical techniques comparing outcomes before and after 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. E aim of this study was to present a technique that directly models the probability of binary events to describe change patterns using linear sections. We describe a modelling method that fits progressively more complex linear sections to the time-series of binary variables. E 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. By accommodating for temporal trends, ITS enables more reliable conclusions but can suffer from bias because its application is focused on a designated point in time (usually the point of the intervention). While statistical variations that aim to rectify these shortcomings do exist, they tend to be too mathematically complex or too arbitrary to be reliable and amenable to healthcare researchers [4]
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