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MDSC 643.02 Biostatistics II

MDSC 643.02 Biostatistics II. Gordon Hilton Fick ghfick@ucalgary.ca www.ucalgary.ca/~ghfick 220-6939. Teaching Assistants. Jian Kang: Office hours and some large group sessions Shelly Vik: Marking and grading. Stata. First: Phone Stata Corporation: 800-248-8272

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MDSC 643.02 Biostatistics II

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  1. MDSC 643.02Biostatistics II Gordon Hilton Fick ghfick@ucalgary.ca www.ucalgary.ca/~ghfick 220-6939

  2. Teaching Assistants • Jian Kang: • Office hours and some large group sessions • Shelly Vik: • Marking and grading

  3. Stata • First: • Phone Stata Corporation: 800-248-8272 • Intercooled Stata 8 is recommended • Perpetual license is the best deal: US$134 • You want ‘Method 3 Grad Plan’ • Then: • Vicki Stagg will contact you • vlstagg@ucalgary.ca • 220-7265

  4. Appointments to see GHF • Crystal Elliott • elliottc@ucalgary.ca • 220-4288 • Monday afternoons are best • Other days and/or times are possible too

  5. Assignments • Interpretation most important • All Stata analyses must be explained • Define all terms in context • Use Equations editor to make symbols

  6. Prerequisite Course • MDSC 643.01: Biostatistics I • B+ or better • Within the past 3 years • See GHF if you have other preparation

  7. Prerequisite material Interpret accurately and completely: • boxplots, scatterplots, line plots, axes, units, titles • mean versus median: when? • data transformations • tests and confidence intervals • tables: FET versus tests • means: t-tests, analysis of variance • matched analysis

  8. Computing Background • Windows/Mac/Linux architecture • Website: links, left versus right clicks • Stata : Rabe-Hesketh & Everitt • : Chapters 1 & 2

  9. Projects • Grouped In pairs • Power Point presentations • To display biostatistical content • 10 minutes for each group

  10. Course Objectives • Health Studies: From idea, to design, to data collection, data analysis and interpretation • Linking classical analyses to model-based analyses • Using software: spreadsheet/database to statistical analysis • Linear regression and Logistic regression: choosing, assessing, interpreting • Introducing other regression models: conditional logistic, proportional hazards and others • Using models to assess potential confounders and/or modifiers

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