| Autor: |
Das, Kishor |
| Přispěvatelé: |
Newell, John, Oliveira, Thiago |
| Rok vydání: |
2023 |
| Předmět: |
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| Popis: |
A method comparison study compares two or more different methods measuring some quantity of interest and the aim is to determine the level of agreement between the methods. Most of the methodological developments in this research area have focused on studies where the response variable of interest is a univariate continuous response. More recently there has been interest on method comparison studies where the response is functional in nature (i.e. a discrete realisations of an underlying functional form). This is still a new and growing field of research in Statistics with many open questions. The specific aim of this thesis is to extend the Linear Mixed Modelling (LMM) framework to method agreement studies i) involving a continuous response when the observed bias between the methods of measurement is non-linear and ii) where the response variable is functional in nature. Initially the focus is on method comparison studies involving a univariate continuous response where the LMM is used to extend the “classical” Bland and Altman approach to account for non-linear bias between two methods of measurement. Following this, a further extension of the LMM framework is proposed for method comparison studies (of increasing design complexity) involving functional responses. A natural alternative analytical approach to consider is the use of Functional Data Analysis (FDA), given the nature of the response. A detailed description of the use of FDA in method comparison studies is given including a new functional equivalent to the Bland-Altman plot. An approach to adapt the LMM framework to accommodate functional responses is then given and the benefits of this approach for study designs with increasing complexity are discussed. The computational issues that arise when fitting a nonparametric LMM are highlighted and a new elegant solution to circumvent this problem is proposed using an eigenbasis for the random-effects regressor matrix. A simulation study is presented to investigate the performance of the FDA and LMM when used to generate functional limits of agreement in studies with no replicates. The performance of the eigenbasis approach to a full B-spline basis implementation is compared in terms of coverage and computational time. All the graphical and analytical approaches proposed are demonstrated using data from two case studies in elite sports: one relating to blood biomarkers with a univariate continuous responses and the other a comparison of two motion capture systems with functional responses. 2025-01-09 |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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