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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