Seminar October 18 , 2002 - High Performance Computing ... · Biostatistics for Dummies Biomedical...
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Transcript of Seminar October 18 , 2002 - High Performance Computing ... · Biostatistics for Dummies Biomedical...
Common Applications(Medical and otherwise)
Clinical medicineEpidemiologicstudiesBiological laboratory researchBiological field researchGenetics
Environmental healthHealth servicesEcologyFisheriesWildlife biologyAgricultureForestry
Biostatisticians Work
Develop study designConduct analysisOversee and regulateDetermine policyTraining researchersDevelopment of new methods
Some Statistics on Biostatistics
Internet search (Google) > 210,000 hits
> 50 Graduate Programs in U.S.
Too much to cover inone hour!
Center Focus
MSU strengthsl Computational
simulation in physical sciences
l Environmental health sciences
Bioinformatics is crowded
Computational simulation in environmental health sciencesl Build on appreciable
MSU strengthl Establish ourselves
l Unique capability l Particular appeal to
NIEHS
Focus of Seminar
Statistical methodologiesl Computational simulation in environmental
health sciencesl Can be classified as “biostatistics”
Stochastic modelingl Time seriesl Spatial statistics*
The Application
Of interestl Cancer incidence ratel Pesticide exposure
Of concernl Agel Genderl Racel Socioeconomic status
Objectivesl Suitably adjust
cancer incidence rate
l Determine if relationship exists
l Develop modell Explain relationshipl Estimate cancer ratel Predict cancer rate
The Data
N.S.S. & U.S. Dept. of Commerce National T.I.S. (1972-2001, by county)l Number of acres
harvestedl Type of crop
MS State Dept. Health Central Cancer Registry (1996 – 1998, by person)l Tumor typel Agel Genderl Racel County of residencel Cancer morbidity
l Crude incidence/100,000
l Age adjusted incidence/100,000
Why (Bio)statistics?
Statisticsl Science of uncertaintyl Model order from
disorder
Disorder existsl Large scale rational
explanationl Smaller scale residual
uncertainty
Chaos
Deterministic equation Randomness
x0
Entropy
(Bio)statistical Data
Independent identically distributed Inhomogeneous dataDependent datal Time seriesl Spatial statistics
Objectives in Time Series
Graphical descriptionl Time plotsl Correlation plotsl Spectral plots
ModelingInferencePrediction
Time Series Models
Linear Models Covariance stationaryl Constant meanl Constant variancel Covariance function
of distance in timee(t) ~ i.i.dl Zero meanl Finite variance
f square summable
Nonlinear Time Series
Amplitude-frequency dependenceJump phenomenonHarmonicsSynchronizationLimit cycles
Biomedical applicationsl Respirationl Lupus-erythematosis l Urinary introgen
excretionl Neural sciencel Human pupillary
system
Some Nonlinear Models
Nonlinear ARl Additive noise
Threshold l ARl Smoothed TARl Markov chain drivenl Fractals
Amplitude-dependent exponential ARBilinearAR with conditional heteroscedasticityFunctional coefficient AR
Describing Correlation
Autocorrelationl AR: exponential decayl MA: 0 past q
Partial autocorrelationl AR: 0 past pl MA: exponential decay
Cross-correlationRelationship to spectral density
Spatial Statistics*
Data componentsl Spatial locations
S = {s1,s2,…,sn}l Observable variable
{Z(s1),Z(s2),…,Z(sn)}l sÎ D Ì Rk
Correlation
Data structuresl Geostatisticall Latticel Point patterns or
marked spatial point processes
l Objects
Assumptions on Zand D
Biological Applications
Geostatisticsl Soil sciencel Public health
Latticel Remote sensingl Medical imaging
Point patternsl Tumor growth ratel In vitro cell growth