Abstract
Many medical (and ecological) processes involve the change of shape, whereby one trajectory changes into another trajectory at a specific time point. There has been little investigation into the study design needed to investigate these models. We consider the class of fixed effect change-point models with an underlying shape comprised two joined linear segments, also known as broken-stick models. We extend this model to include two sub-groups with different trajectories at the change-point, a change and no change class, and also include a missingness model to account for individuals with incomplete follow-up. Through a simulation study, we consider the relationship of sample size to the estimates of the underlying shape, the existence of a change-point, and the classification-error of sub-group labels. We use a Bayesian framework to account for the missing labels, and the analysis of each simulation is performed using standard Markov chain Monte Carlo techniques. Our simulation study is inspired by cognitive decline as measured by the Mini-Mental State Examination, where our extended model is appropriate due to the commonly observed mixture of individuals within studies who do or do not exhibit accelerated decline. We find that even for studies of modest size (n = 500, with 50 individuals observed past the change-point) in the fixed effect setting, a change-point can be detected and reliably estimated across a range of observation-errors.
Highlights
When observing a changing outcome over time, using longitudinal data, the process may contain periods in which a marked or distinct change occurs in the underlying shape of the data
Change-point models – known as change-point regression, switching regression,[1] changing regression,[2] two-phase regression, segmented regression, broken-stick regression, turning points[3] or bent-cable regression4 – encompass a wide class of problems. They have been fitted to many longitudinal processes such as: modelling distinct changes in the rates of a Poisson process for mining accidents,[5,6] changes in economic time-series trends,[7] extremes of climate,[8] modelling cognitive decline,[9] effect of calcium supplementation on blood pressure,[10] CD4 Tcell counts for HIV-infected individuals[11] and biomarker levels for prostate cancer[12,13]
Our investigation is motivated by the study of cognitive decline in ageing; using this setting, we define the parameter ranges considered in our simulation study
Summary
When observing a changing outcome over time, using longitudinal data, the process may contain periods in which a marked or distinct change occurs in the underlying shape of the data. Change-point models – known as change-point regression, switching regression,[1] changing regression,[2] two-phase regression, segmented regression, broken-stick regression, turning points[3] or bent-cable regression4 – encompass a wide class of problems They have been fitted to many longitudinal processes such as: modelling distinct changes in the rates of a Poisson process for mining accidents,[5,6] changes in economic time-series trends,[7] extremes of climate,[8] modelling cognitive decline,[9] effect of calcium supplementation on blood pressure,[10] CD4 Tcell counts for HIV-infected individuals[11] and biomarker levels for prostate cancer[12,13] (see annotated bibliographies and overviews[14,15,16,17]). Some individuals experience a period of steep decline, so-called accelerated decline
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