Section outline

  • Lecture: Evaluating lifecourse determinants of chronic disease in longitudinal data analysis


    Faculty:  Maria Glymour

    Location:      Rock Hall 102

    • Session Slides:

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

    • Watch URL

    • Required Reading:

      1. Ben-Shlomo Y, Kuh D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. International journal of Epidemiology 2002;31(2):285-293.

      2.   Mishra G, Nitsch D, Black S, De Stavola B, Kuh D, Hardy R. A structured approach to modelling the effects of binary exposure variables over the life course. International journal of epidemiology 2009;38(2):528-537.

      3. Naumova, E., A. Must, et al. (2001). "Tutorial in Biostatistics: Evaluating the impact of critical periods' in longitudinal studies of growth using piecewise mixed effects models." International Journal of Epidemiology 30(6): 1332.

      4. Wills AK, Lawlor DA, Matthews FE, Aihie Sayer A, Bakra E, Ben-Shlomo Y, Benzeval M, Brunner E, Cooper R, Kivimaki M, Kuh D, Muniz-Terrera G, Hardy R. Life Course Trajectories of Systolic Blood Pressure Using Longitudinal Data from Eight UK Cohorts. PLoS Med 2011;8(6):e1000440.

      5. Fitzpatrick A, Kuller L, Lopez O, et al. Midlife and late-life obesity and the risk of dementia: cardiovascular health study. Archives of Neurology. 2009;66(3):336

       

    • Optional Reading:

    • Naumova CriticalPeriods File
      Not available unless: Your ID number contains 02
    • Mishra methods lifecourse File
      Not available unless: Your ID number contains 02
    • Fitzpatrick ObesityDementia File
      Not available unless: Your ID number contains 02
    • Willis LIfecourseBP File
      Not available unless: Your ID number contains 02
    • Ben Shlomo Kuh Lifecourse File
      Not available unless: Your ID number contains 02

    • Assignment: For a specific exposure-outcome combination of interest to you, specify which lifecourse model is likely most appropriate and why you think this is the case. Describe the regression models you could use to test your hypothesis. Are there any possible data sets in which this test could be conducted, and if so, what concerns would you have about interpreting your proposed test of the lifecourse model?

      Optional Additional Assignment for the Applied Data Analysis IS:

      Select a variable that characterizes states that you think potentially related to disability (e.g., state average infant mortality rate; state voter participation rates; state unemployment rate).  Choose a variable for which you can identify a data set with historical trend data for US states. Propose two alternative hypotheses that you can test with your data about potentially relevant exposure ages.  For example: "state infant mortality rate in the year of the individual's birth will predict self-care disability at age 30-40" vs "state infant mortality rate in the year the individual turned 18 will predict self-care disability at age 30-40".  You can alter the age of the outcome or the age of exposure for your hypotheses, but you will probably be constrained by the data sources you are able to access.  Link the external data to your census data and test your two alternative hypotheses.  Describe your hypothesis, analysis approach, and findings. 

    • updated slides class 4 and 5 File