Measuring the reproducibility of and comparability between physiological and psychological responses in exercise testing
Chapter 1 gives a brief background to Exercise Testing and its importance as well as a literature review of relevant topics including reproducibility, comparability, components of variance and the estimation of common correlation; the latter two are essential building blocks for the estimation of Comparability. Chapter 2 deals with the estimation of measurement reproducibility of data from mixed effects models involving two variance components. Two approaches, one based on sums of squares and the other on Profile Likelihood are used for the separate cases of balanced and unbalanced data. This is carried out in two distinct contexts, one for simple replication and the other assuming an order effect to the replications. Applicability of the approaches to Exercise Testing data shows that while point estimates from both approaches are often identical, interval estimates from the Profile Likelihood approach tend to be narrower. Chapter 3 involves a simulation study to investigate and assess the performances of the two approaches. Data are simulated from a variety of underlying configurations and the performances then compared according to three statistical criteria. The results of this study again favour the Profile Likelihood approach. The estimation of Comparability between two variables is the other aspect of the thesis put forward in chapter 4 where, first of all, the estimation of a common correlation coefficient from a population of correlation coefficients is considered. Five different methods for point and interval estimation of a common correlation coefficient are introduced. An illustrative example using data from an Exercise Testing procedure is used to compare the performances of the methods. Further investigation on the performances of the five methods was carried out by means of a simulation study across a variety of underlying configurations. The overall results suggest the 'Fisher method' as the best method of point and interval estimate of common correlation. Finally, chapter 6 outlines the conclusions from the previous chapters and suggests some ideas for further work.