@MASTERSTHESIS\{IMM2007-05563,
    author       = "X. Liu",
    title        = "A Validation Study for an Enzyme Analytical Method",
    year         = "2007",
    keywords     = "Inter-laboratory Validation Study, Linear Mixed Model, Homogeneous Variance Model, Heterogeneous Variance Model, Repeatability and Reproducibility, Assumptions Checking",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    note         = "Supervised by Helle Rootzen, and Poul Thyregod, {IMM,} {DTU}.",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5563-full.html",
    abstract     = "In 2005 a new methodology has been developed to evaluate the activity of the phytase enzyme in liberating phytate bound phosphorus in animal feed. In this thesis validation study of this new method is performed. Two times of inter-laboratory studies with similar design were conducted. The larger inter-laboratory study involving more Labs and Materials is the main analysis object. The analysis of smaller study is also performed as a contrast with the larger one.

Different variance structures of Linear Mixed Models are presented, such as Homogeneous Variance Model and Heterogeneous Variance Model, to detect variability characteristics of measurement error. The main characteristics of performance precision are Repeatability and Reproducibility. Besides that, whether the Type (Solid/Liquid) of the Materials effects the evaluation is another topic of interest. A guess that the liquid materials may have more stable measurements and smaller variances will be investigated by Linear Mixed Models. The homogeneity of the Labs’s capability in the evaluation is also investigated.

Usually mathematical models have to satisfy the assumptions. But in most cases interlaboratory data could not satisfy the strict assumptions. The objectives of most validation studies are to reveal useful information and parameters of the data rather than finding a fitting model. Whereas this thesis also emphasizes particularly on modeling, which could supply more general characteristics of data.

From Homogeneous Variance models to Heterogeneous Variance Models, most of these models presented in this thesis were proven not to satisfy the assumptions. But the modeling and assumption checking process could supply great details of data, which could be the indication of modifying the covariance structures of models."
}