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Sample size and classification error for Bayesian change-point models with unlabelled sub-groups and incomplete follow-up

Lookup NU author(s): Professor Fiona MatthewsORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


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.

Publication metadata

Author(s): White SR, Muniz-Terrera G, Matthews FE

Publication type: Article

Publication status: Published

Journal: Statistical Methods in Medical Research

Year: 2018

Volume: 27

Issue: 5

Pages: 1476-1497

Print publication date: 01/05/2018

Online publication date: 08/08/2016

Acceptance date: 04/06/2016

Date deposited: 19/10/2016

ISSN (print): 0962-2802

ISSN (electronic): 1477-0334

Publisher: Sage Publications Ltd.


DOI: 10.1177/0962280216662298

PubMed id: 27507286


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