Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Biostatistics for Dummies

7,577 views

Published on

Published in: Technology, Economy & Finance
  • Be the first to comment

Biostatistics for Dummies

  1. 1. Biostatistics for Dummies Biomedical Computing Cross-Training Seminar October 18th , 2002
  2. 2. What is “Biostatistics”? Techniques Mathematics Statistics Computing Data Medicine Biology
  3. 3. What is “Biostatistics”? Biological data Knowledge of biological process
  4. 4. Common Applications (Medical and otherwise) Clinical medicine Epidemiologic studies Biological laboratory research Biological field research Genetics Environmental health Health services Ecology Fisheries Wildlife biology Agriculture Forestry
  5. 5. Biostatisticians Work Develop study design Conduct analysis Oversee and regulate Determine policy Training researchers Development of new methods
  6. 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. 7. Center Focus 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
  8. 8. Focus of Seminar Statistical methodologies Computational simulation in environmental health sciences Can be classified as “biostatistics” Stochastic modeling Time series Spatial statistics*
  9. 9. The Application 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
  10. 10. The Data 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
  11. 11. Why (Bio)statistics? Statistics  Science of uncertainty  Model order from disorder Disorder exists  Large scale rational explanation  Smaller scale residual uncertainty Chaos Deterministic equation Randomness x0 Entropy
  12. 12. (Bio)statistical Data Independent identically distributed Inhomogeneous data Dependent data Time series Spatial statistics
  13. 13. Time Series Identically distributed Time dependent Equally spaced Randomness
  14. 14. Objectives in Time Series Graphical description Time plots Correlation plots Spectral plots Modeling Inference Prediction
  15. 15. Time Series Models Linear Models Covariance stationary  Constant mean  Constant variance  Covariance function of distance in time(t) ~ i.i.d  Zero mean  Finite variance  square summable
  16. 16. Nonlinear Time Series Amplitude-frequency dependence Jump phenomenon Harmonics Synchronization Limit cycles Biomedical applications  Respiration  Lupus-erythematosis  Urinary introgen excretion  Neural science  Human pupillary system
  17. 17. Some Nonlinear Models Nonlinear AR  Additive noise Threshold  AR  Smoothed TAR  Markov chain driven  Fractals Amplitude- dependent exponential AR Bilinear AR with conditional heteroscedasticity Functional coefficient AR
  18. 18. A Threshold Model
  19. 19. A Threshold Model
  20. 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. 21. Spatial Statistics* 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
  22. 22. Biological Applications Geostatistics  Soil science  Public health Lattice  Remote sensing  Medical imaging Point patterns  Tumor growth rate  In vitro cell growth
  23. 23. Spatial Temporal Models Combine time series with spatial data Application Time element time Pesticide exposure develop cancer  Spatial element  Proximity to pesticide use

×