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Equation Modeling , Generalized Regression , Functional Data Explorer , Linear Mixed Models , Flare Link , Gator Link ,

Predicting Hospital Readmission in Medicaid Patients With Diabetes Using Administrative and Claims Data

A statistical model based entirely on claims data can accurately predict 30-day hospital readmission in Medicaid patients with diabetes. ....

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"Information based diagnostics for the optimal construction of Multi-en" by Christopher Lisle

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 appropriate dataset. This thesis fills a void in the literature by providing information-based diagnostics for the optimal construction of the MET dataset.
The approaches are demonstrated using two motivating datasets: the first is a Oat (Avena sativa) dataset and the other is a Durum wheat (Triticum durum L. ssp. Durum Desf.) dataset. The former is used as an example of a dataset with independent variet ....

Multi Environment Trials , Linear Mixed Models , Model Based Design , Plant Breeding ,

"Empirical comparison of time series models and tensor product penalise" by Beverley Gogel, Sue Welham et al.

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 variation in field trial data. This represents a non-stochastic smoothing approach which is in contrast to the autoregressive (AR) approach which models a stochastic covariance structure between the lattice of errors. This paper compares the AR and TPS methods empirically for a large set of early generation plant breeding trials. Here, the fitted models include information on genetic relatedness amon ....

Akaike Information Criteria , Rima Time Series Models , Genetic Relatedness , Linear Mixed Models , Eparable Lattice Process , Spatial Dependence , Ensor Product Penalised Spline ,

"Information Based Diagnostic for Genetic Variance Parameter Estimation" by Chris Lisle, Alison Smith et al.

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 VE effects. In this paper we have provided the link between the objectives of this work and those in model-based experimental design. We propose the use of the (Formula presented.) -optimality criterion as a diagnostic to capture the information available for the residual maximum likelihood (REML) estimation of the genetic variance parameters. We demonstrate the methods for a dataset with pedigree ....

D Optimality , Linear Mixed Models , Multi Environment Trials , Simulation Study , Ariety Connectivity ,