Section outline

  • Lecture: Clustered Data: Person level clustering
     This lecture will finish the threats to validity discussion from week 1 and continue onto a discussion of clustered data, focusing on clustering due to spatial autocorrelation or repeated measures on the same person. 

    Faculty: Maria Glymour

    Location: Rock Hall 102

    • Session Slides:

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

    • Watch URL

    • Required Reading:

      1. Wilson RS, Hebert LE, Scherr PA, Barnes LL, Mendes de Leon CF, Evans DA. Educational attainment and cognitive decline in old age. Neurology. 2009;72(5):460.
      2. Hanley et al., Statistical analysis of correlated data using GEE: an orientation.  Am J Epi 2003 v 157, pg 364. 
      3. Hubbard et al To GEE or not to GEE.  Comparing Population Average and Mixed Models for Estimating ASsociations Between Neighborhood Risk Factors and Health. Epidemiology 2010. 
    • wilson edn cogDecline File
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    • Hubbard GEE File
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    • Hanley GEE Am. J. Epidemiol.-2003-Hanley-364-75 File
      Not available unless: Your ID number contains 02

    • Optional Reading:  Interaction methods are central to growth curve models.  If you are rusty on interactions, the VanderWeele article is highly recommended.

    • VanderWeele.InteractionTutorial 2014 File
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    • Optional reading URL

    • Assignment: 

      Defining the time dimension is a fundamental challenge in longitudinal data analysis.  The most common choice is age or time since study enrollment, however, for many questions other time dimensions are relevant, for example grade in school, or time before or after stroke incidence, or time since release from prison.  Identify an article in the applied literature using a longitudinal analysis based on age as the time dimension, time since study enrollment as the time dimension, and one other possible time dimension (ie, not age or time since study enrollment).  For each study, briefly describe the research question, the study sample, the longitudinal design, and the analysis approach. Please post links to the studies. 

      Additional Assignment for Applied Data Analysis IS: 

      1) Using your Census data set from week 2, estimate the association between age and self-care disability.  Re-estimate the model, controlling for year of birth. Write a paragraph summarizing your analysis approach, findings, and the limitations of this analysis based on cross-sectional data.

      2) Return to the IPUMS site and download the 1990 5% sample, retaining the same core demographics as before and the available disability variables (hint: you can revise your old data request).  

      3) Estimate the same model from step 1 in the 1990 census.  Compare the results.

      4) Pool the two Census data sets. Estimate the same model as in 1 and a new model with an interaction between year of the census and age. Describe your findings.

    • lecture slides File
    • Lecture 3 in class quiz File