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A statistical model based entirely on claims data can accurately predict 30-day hospital readmission in Medicaid patients with diabetes.
The objectives of this thesis are to present novel approaches for optimising the construction of multi environment trial (MET) datasets from a series of plant variety trials. These include evaluating varieties in designed trials at various locations and typically across many years. The MET datasets are then analysed to evaluate how well each variety performs in each environment. Although sophisticated and relevant statistical analyses have been proven to increase the reliability of predicted variety by environment (VE) effects, there has been little research into how to construct an appropriat...
Plant breeding field trials are typically arranged as a row by column rectangular lattice. They have been widely analysed using linear mixed models in which low order autoregressive integrated moving average (ARIMA) time series models, and the subclass of separable lattice processes, are used to account for two-dimensional spatial dependence between the plot errors. A separable first order autoregressive model has been shown to be particularly useful in the analysis of plant breeding trials. Recently, tensor product penalised splines (TPS) have been proposed to model two-dimensional smooth var...
Plant breeding programs evaluate varieties in series of field trials across years and locations, referred to as multi-environment trials (METs). These are an essential part of variety evaluation with the key aim of the statistical analysis of these datasets to accurately estimate the variety by environment (VE) effects. It has previously been thought that the number of varieties in common between environments, referred to as “variety connectivity,” was a key driver of the reliability of genetic variance parameter estimation and that this in turn affected the reliability of predictions of V...
A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the...