Semester 2
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Date |
Presenter |
Title |
Abstract |
1 Jul |
Dr Chuang
Taipei Medical University
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Systems Theory and Health Care: Crossing the Quality Chasm |
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The presentation will provide some of Dr Chuang’s research and
results of applying a system theory-based approach in the development of
appropriate integration and analytical models within the context of the
health care system. Two models will be discussed:
1) the holistic healthcare system relationship model: a framework for
evaluating the effectiveness of the interrelated subsystems of the
healthcare system, namely the accreditation, QMR and HCO health care
systems, on improvements in health care;
2) Systems Oriented Event Analysis model (SOEA): a mechanism of guiding
the events investigation team to be able to integrate systems changes in
the way of systems thinking. Applications of the SOEA model have been
found to succeed where traditional root cause analyses have failed.
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4 Jul |
Dr Patrick McElduff
Hunter Medical Research Institute
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Cluster randomised trials - what do you do when there is a small number of clusters? |
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Randomised controlled trials (RCT) are considered the gold standard for
testing new treatments or interventions, which are designed to improve
the health and care of patients. Typically the unit of randomisation in
an RCT is the individual patient. However in some trials the
intervention is applied at a service level, such as a clinic, hospital
or school and in these trials it is necessary to randomise all patients
at a particular clinic, hospital or school to the same treatment arm.
These trials are generally called cluster RCT and the analysis of data
from these trials must account for the potential correlation of outcomes
between patients within a cluster.
In this seminar I will present some of the issues around the analysis
of these trials with reference to a recent cluster RCT of an
intervention to manage fever, hyperglycemia and swallowing dysfunction
in acute stroke patients in 19 hospitals within NSW.
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5 Aug |
Mr Barrie Stokes
The University of Newcastle
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Maximum Entropy Alternatives to Pearson Family Distributions |
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In the spirit of ET Jaynes' Maximum Entropy Principle, a (Bayesian) prior distribution conforming or constrained, say, to known moments should have the maximum entropy of all such distributions. At a previous MaxEnt conference [MaxEnt 2009, Oxford Mississippi] a method of obtaining MaxEnt univariate distributions under a variety of constraints was presented. The Mathematica function Interpolation[], normally used with numerical data, can also process "semi-symbolic" data, and Lagrange Multiplier equations were solved for a set of symbolic ordinates describing the required MaxEnt probability density function. We apply a developed version of this approach to finding MaxEnt distributions having prescribed beta1 (skewness squared) and beta2 (kurtosis) values, and compare the entropies of the MaxEnt distributions to those of the Pearson family distributions having the same beta1 and beta2. (This work was presented in poster form at the recent MaxEnt 2011 conference in Waterloo, Canada.)
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9 Sep |
Dr John Best
The University of Newcastle
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A Two-Part Talk: 1. Tests for Latin Squares and 2. Tests for Randomized Block Categorical Data |
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Two projects, joint with Prof John Rayner, which are still "work in progress" will be briefly described. The first relates to finding a nonparametric test for replicated latin squares and the second concerns a new algorithm for a Chi-square test of homogeneity when the counts are not based on independent observations (being derived from a randomized block layout). The talk will be at an introductory level and sensory evaluation data will illustrate the methods.
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14 Oct |
Dr Darfiana Nur
The University of Newcastle
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Nonlinear Time Series Modelling in Bionformatics and Finance: Bayesian Perspectives |
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I would like to share some research progress/problems from my recent study leave. There are two main components in this talk: DNA sequence modelling in Bioinformatics and Bayesian analysis of a modified Smooth Threshold Autoregressive (STAR) models in Finance. For the DNA sequence modelling, I continued my research in Bayesian Hidden Markov modelling for some new DNA sequences and extended the research to model short DNA motif segmentation. Computing problems were encountered due to the complexity of the model and the size of DNA data. For the STAR models in Finance, I partly derived a Bayesian analysis of STAR and extended it to STAR-GARCH models of order p by obtaining the posterior distributions of their parameters based on both noninformative and conjugate priors.
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4 Nov |
Chris Oldmeadow
Hunter Medical Research Institute
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Bayesian Networks: overview and two applications in health and genetics |
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Bayesian Networks (BNs) are a class of probabilistic graphical models for reasoning and decision making within systems that contain uncertainty. They are useful for both understanding underlying mechanisms of a system and for updating beliefs when new data are available. In a BN, the variables within the system are depicted as nodes in a Directed Acyclic Graph (DAG) and conditional probability distributions can be assigned by domain experts or estimated from the data. Under certain assumptions, causal dependencies between variables are captured by the DAG and thus these models are very popular for causal inference when only observation data are available.
In this talk I will give a general overview of inference in Bayesian Networks, focusing on the case where the causal structure of the system is assumed to be known. Two applications in the fields of Health and Genetics will be presented.
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