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Biostatistics for Dummies

Biostatistics for Dummies. Very Intelligent People. S. Biomedical Computing Cross-Training Seminar October 18 th , 2002. What is “Biostatistics”?. Techniques Mathematics Statistics Computing Data Medicine Biology. What is “Biostatistics”?. Knowledge of biological process.

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Biostatistics for Dummies

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  1. Biostatistics for Dummies Very Intelligent People S Biomedical Computing Cross-Training Seminar October 18th, 2002

  2. What is “Biostatistics”? • Techniques • Mathematics • Statistics • Computing • Data • Medicine • Biology

  3. What is “Biostatistics”? Knowledge of biological process Biological data

  4. Clinical medicine Epidemiologic studies Biological laboratory research Biological field research Genetics Environmental health Health services Ecology Fisheries Wildlife biology Agriculture Forestry Common Applications(Medical and otherwise)

  5. Biostatisticians Work • Develop study design • Conduct analysis • Oversee and regulate • Determine policy • Training researchers • Development of new methods

  6. Some Statistics on Biostatistics • Internet search (Google) > 210,000 hits • > 50 Graduate Programs in U.S. Too much to cover in one hour!

  7. MSU strengths Computational simulation in physical sciences Environmental health sciences Bioinformatics is crowded Computational simulation in environmental health sciences Build on appreciable MSU strength Establish ourselves Unique capability Particular appeal to NIEHS Center Focus

  8. Focus of Seminar • Statistical methodologies • Computational simulation in environmental health sciences • Can be classified as “biostatistics” • Stochastic modeling • Time series • Spatial statistics*

  9. Of interest Cancer incidence rate Pesticide exposure Of concern Age Gender Race Socioeconomic status Objectives Suitably adjust cancer incidence rate Determine if relationship exists Develop model Explain relationship Estimate cancer rate Predict cancer rate The Application

  10. N.S.S. & U.S. Dept. of Commerce National T.I.S. (1972-2001, by county) Number of acres harvested Type of crop MS State Dept. Health Central Cancer Registry (1996 – 1998, by person) Tumor type Age Gender Race County of residence Cancer morbidity Crude incidence/100,000 Age adjusted incidence/100,000 The Data

  11. Statistics Science of uncertainty Model order from disorder Disorder exists Large scale rational explanation Smaller scale residual uncertainty Chaos Why (Bio)statistics? • Entropy x0 Deterministic equation Randomness

  12. (Bio)statistical Data • Independent identically distributed • Inhomogeneous data • Dependent data • Time series • Spatial statistics

  13. Time Series • Identically distributed • Time dependent • Equally spaced Randomness October 18th 2002

  14. Objectives in Time Series • Graphical description • Time plots • Correlation plots • Spectral plots • Modeling • Inference • Prediction

  15. Linear Models Covariance stationary Constant mean Constant variance Covariance function of distance in time Time Series Models • (t) ~ i.i.d • Zero mean • Finite variance •  square summable

  16. Amplitude-frequency dependence Jump phenomenon Harmonics Synchronization Limit cycles Biomedical applications Respiration Lupus-erythematosis Urinary introgen excretion Neural science Human pupillary system Nonlinear Time Series

  17. Nonlinear AR Additive noise Threshold AR Smoothed TAR Markov chain driven Fractals Amplitude-dependent exponential AR Bilinear AR with conditional heteroscedasticity Functional coefficient AR Some Nonlinear Models

  18. A Threshold Model

  19. A Threshold Model

  20. Describing Correlation • Autocorrelation • AR: exponential decay • MA: 0 past q • Partial autocorrelation • AR: 0 past p • MA: exponential decay • Cross-correlation • Relationship to spectral density

  21. Data components Spatial locations S = {s1,s2,…,sn} Observable variable {Z(s1),Z(s2),…,Z(sn)} s D  Rk Correlation Data structures Geostatistical Lattice Point patterns or marked spatial point processes Objects Assumptions on Z and D Spatial Statistics*

  22. Biological Applications • Geostatistics • Soil science • Public health • Lattice • Remote sensing • Medical imaging • Point patterns • Tumor growth rate • In vitro cell growth

  23. Spatial Temporal Models • Combine time series with spatial data • Application • Time element time • Pesticide exposure develop cancer • Spatial element • Proximity to pesticide use

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