Section outline

  • Lecture:  Clustered Data Arising from Repeated Measures or Contextual Effects
     This lecture will discuss using random effects/multilevel models for neighborhood effects estimation.

    Faculty:  Maria Glymour

    Location:     Mission Hall 1406

    • Session Slides:

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch URL

    • Required Reading:

      1. Singer, J. D. (1998). "Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models." Journal of Educational and Behavioral Statistics 24(4): 323-355.
      2. Arcaya M (2013) "Effects of Proximate Foreclosed Properties on Individuals’ Weight Gain in Massachusetts, 1987–2008"  Am J Public Health
      3. Lee (2012) "Length of Inpatient Stay of Persons With Serious Mental Illness: Effects of Hospital and Regional Characteristics" Psychiatric Services 63, pg 899.
      4. Greenland S (2000) Principles of multilevel modeling. Intl J Epidemiology. 29: 158.

      Singer et al is a classic and brilliant article by one of the great popularizers of multilevel models.  It is worth reading several times.  Arcaya and Lee are examples of common applications of multilevel models to illustrate the types of questions people approach with these models.  Lee in particular illustrates how the lowest unit of observation does not need to be an individual. 

      Greenland's framing is unusual but extremely helpful because it makes the link between multilevel models and Bayesian frameworks. 

    • Arcaya File
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    • Lee File
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    • Singer File
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    • Greenland File
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    • Optional Reading:

    • Assignment: Find any article using clustered data and describe: the unit of clustering; the hypothesized effects and the level at which the exposure is measured (is it a characteristic of the cluster or the observation within the cluster); and the statistical model used to estimate the effect.  Describe whether there are any other statistical models that might be appropriate and whether they would be preferable (e.g., GEE vs mixed).

    • Lecture 2 slides File
    • Quiz for Lecture 2 File
    • slides for lecture 2 - updated as of 4/11 File