Clinical Decision Support

Within orthopædics, clinicians routinely take multiple measurements on patients during the course of their treatment, often repeating the same measurements before and after operations, and subsequently at periodic follow-up consultations. This data combined with additional factors gives a wealth of information, resulting in a high-dimensional data set with a mixture of data types and a longitudinal aspect; all of which can be problematic in statistical analysis. Therefore, general statistical methods for the investigation and analysis of a generic medical data set are presented and developed. Methods are proposed for supporting exploratory analysis of the data via novel visualisations of the patient’s status over time across multiple variables, thus giving an easily interpretable overview of this evolution. To address the problem of high dimensionality of the data, a new approach to variable selection is proposed and developed using principal variables. The method is further extended by the use of temporal smoothing to tackle data with this repeated measures aspect allowing for the simultaneous reduction of the patient status variables over time. The ultimate goal of these analyses is to determine an appropriate model for the orthopædic data, with a focus on the modelling of the time series of patient progress. The techniques of graphical modelling and, in particular, those of chain graphs lend themselves to this problem. Additionally, they have the added benefit of a simple and intuitive visualisation which is of benefit to clinicians. All of these methods are illustrated via their application to two large-scale case study data sets concerning total joint replacement. i