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Activity Number:
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122
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Type:
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Invited
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Date/Time:
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Monday, August 4, 2008 : 10:30 AM to 12:20 PM
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Sponsor:
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International Society of Bayesian Analysis
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| Abstract - #300167 |
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Title:
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Bayesian Modeling of Neuron Death
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Author(s):
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Anthony Pettitt+ and Gareth Ridall*+
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Companies:
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Queensland Univeristy of Technology and Lancaster University
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Address:
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Mathematical Sciences, Brisbane, International, 4069, Australia Maths and Stats, Lancaster, LA1 4YW, United Kingdom
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Keywords:
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neuron death ; model choice ; informative prior ; Bayes factor ; motor neuron disease
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Abstract:
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Amyotrophic lateral sclerosis (ALS), a type of motor neuron disease, is a degenerative neurological disease. Mathematical models have been put forward to explain the death process for the numbers of surviving nerve cells. For ALS Ridall et al (Appl Statistics, 2007) developed a Bayesian model to allow clinicians to assess disease progress by estimating a patient's remaining number of motor units by analyzing data measured from a given muscle in response to electrical stimulation of a nerve. In that approach many informative priors were used but a flat prior was used for the number of units. Data from previous studies carried out with the patient were considered separately. We investigate the use of a two stage process to incorporate previous patient data. We use the Bayes factor to distinguish between competing models of neuron death which have different biological interpretations.
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