Modelling the effect of a dedicated hip fracture unit on patient outcomes using segmented robust linear regression techniques.

Introduction: Typically, a healthcare intervention is evaluated by comparing data before and after its implementation using statistical tests. Comparing group means can miss underlying trends and lead to erroneous conclusions. Segmented linear regression can be used to reveal secular trends but is susceptible to outliers.

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 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.

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 aimed to develop a rigorous mathematical method to analyse temporal variation and overcome these limitations.

Modeling Learning in Surgical Practice.

Objective: Methods that model surgical learning curves are frequently descriptive and lack the mathematical rigor required to extract robust, meaningful, and quantitative information. We aimed to formulate a method to model learning that is tailored to dealing with the high variability seen in surgical data and can readily extract important quantitative information such as learning rate, length of learning, and learnt level of performance.

Methods: We developed a method where progressively more complex models are fitted to learning data.