biostatistics seminar
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Biostatistics for Dummies
Biomedical Computing Cross-Training
SeminarOctober 18th, 2002
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What is Biostatistics?
Techniquesl Mathematics
l Statistics
l Computing
Datal Medicine
l Biology
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What is Biostatistics?
Biological data
Knowledge of
biological process
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Common Applications
(Medical and otherwise)
Clinical medicine
Epidemiologic
studiesBiological laboratory
research
Biological fieldresearch
Genetics
Environmental
health
Health servicesEcology
Fisheries
Wildlife biologyAgriculture
Forestry
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Some Statistics on
BiostatisticsInternet search (Google)
> 210,000 hits> 50 Graduate Programs in U.S.
Too much to cover in
one hour!
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Center Focus
MSU strengths
l Computational
simulation inphysical sciences
l Environmental health
sciences
Bioinformatics iscrowded
Computational
simulation in
environmentalhealth sciences
l Build on appreciable
MSU strength
l Establish ourselvesl Unique capability
l Particular appeal to
NIEHS
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Focus of Seminar
Statistical methodologies
l Computational simulation in environmental
health sciencesl Can be classified as biostatistics
Stochastic modeling
l Time seriesl Spatial statistics*
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The Application
Of interest
l Cancer incidence rate
l Pesticide exposureOf concern
l Age
l Gender
l Race
l Socioeconomic status
Objectives
l Suitably adjust
cancer incidencerate
l Determine if
relationship exists
l Develop modell Explain relationship
l Estimate cancer rate
l Predict cancer rate
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The Data
N.S.S. & U.S. Dept. ofCommerce National
T.I.S. (1972-2001, by
county)l Number of acres
harvested
l Type of crop
MS State Dept. HealthCentral Cancer Registry(1996 1998, by person)
l Tumor typel Age
l Gender
l Race
l County of residence
l Cancer morbidityl Crude
incidence/100,000
l Age adjustedincidence/100,000
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Why (Bio)statistics?
Statistics
l Science of uncertainty
l Model order fromdisorder
Disorder exists
l Large scale rational
explanationl Smaller scale residual
uncertainty
Chaos
Deterministic
equation Randomness
x0
Entropy
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(Bio)statistical Data
Independent identically distributed
Inhomogeneous data
Dependent data
l Time series
l Spatial statistics
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Time SeriesIdentically distributed
Time dependent
Equally spaced Randomness
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Objectives in Time Series
Graphical description
l Time plots
l Correlation plots
l Spectral plots
Modeling
Inference
Prediction
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Time Series Models
Linear Models Covariance
stationary
l Constant meanl Constant variance
l Covariance function
of distance in timee(t) ~ i.i.d
l Zero meanl Finite variance
f square summable
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Nonlinear Time Series
Amplitude-frequency
dependence
Jump phenomenonHarmonics
Synchronization
Limit cycles
Biomedical
applications
l Respirationl Lupus-erythematosis
l Urinary introgen
excretion
l Neural sciencel Human pupillary
system
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A Threshold Model
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A Threshold Model
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Describing Correlation
Autocorrelation
lAR: exponential decay
l MA: 0 past q
Partial autocorrelation
lAR: 0 past p
l MA: exponential decay
Cross-correlation
Relationship to spectral density
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Spatial Statistics*
Data components
l Spatial locations
S= {s1,s2,,sn}l Observable variable
{Z(s1),Z(s2),,Z(sn)}
l s D Rk
Correlation
Data structures
l Geostatistical
l Latticel Point patterns or
marked spatial point
processes
l ObjectsAssumptions on Z
and D
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Biological Applications
Geostatistics
l Soil science
l Public healthLattice
l Remote sensing
l Medical imaging
Point patterns
l Tumor growth rate
l In vitrocell growth
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Spatial Temporal Models
Combine time series with spatial data
Application
l Time elementtime
l Pesticide exposure develop cancer
l Spatial element
l Proximity to pesticide use
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