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BIOST 540, Spring 2010
Analysis of Correlated Data
Assignments
 
  • Week 1:
    • Reading:
      • Diggle, Heagerty, Liang and Zeger (DHLZ) Chapter 1 (HS Library Reserve)
      • LDAchapter.pdf --- Overview chapter from van Belle, Fisher, Heagerty and Lumley (2004)
      • BurtonEtAl1998.pdf --- Tutorial in Biostatistics, from Burton et al. (1998), Stat in Medicine (Section 1, 2.1-2.4).
    • Application: please locate a journal article that uses longitudinal or multilevel data analysis methods (e.g. look for "change" analysis, or mixed models, or GEE) -- be prepared to discuss the following aspects:
      • Population
      • Scientific question(s)
      • Analysis approaches
      • Issues (missing data, model selection, time-dependent covariate, etc.)
    • Summary Form for Application: CorrelatedDataPaperReview.pdf

  • Week 2:
    • Reading:
      • DHLZ Chapter 2 (Sections 2.1-2.3), FLW Chapter 3 (Sections 3.1-3.5)
      • Fitzmaurice2001.pdf --- A Conundrum in the Analysis of Change, Fitzmaurice (2001)
      • FrisonPocock1992.pdf --- Repeated measures in clinical trials: analysis using mean summary statistics and its implications for design, Frison and Pocock (1992)

  • Week 3:
    • Reading:
      • DHLZ Chapter 6 (Sections 6.1-6.3)
    • Application: See attached assignment, ASM03.pdf, here.

  • Week 4:
    • Reading:
      • DHLZ Chapter 3, FLW Chapter 6

  • Week 5:
    • Reading:
      • DHLZ Chapters 7-8, FLW Chapter 11
    • Application: See attached assignment, ASM04.pdf, here.

  • Week 6:
    • Reading:
      • DHLZ Chapters 7.2, 9, FLW Chapter 8
    • Application: See the Sheffe Illustration in the Data directory.

  • Week 7:
    • Reading:
    • Application: See attached assignment, ASM05.pdf, here.

  • Week 8:
    • Reading:
      • DHLZ Chapter 10
    • Application: See attached assignment, ASM06.pdf, here.

  • Week 9:
    • Reading:
      • DHLZ Chapter 13, FLW Chapter 14
      • Optional reading: T.E. Raghunathan (2004) - Annual Review of Public Health. Discusses methods for general types of missing data: Raghunathan (2004)
    • Application: See missing data illustrations in Week #9 lectures.