Section outline

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  • Monday, March 28, 2016; 10:30 AM - 12:30 PM

    Lecture: Causal Inference in the Context of Observational Data: identifying threats to validity

    This class introduces the overall framework of causal inference from observational data and compares the motivation typically given in modern epidemiology with traditional accounts of causation, including the very influential Cook & Campbell framework and the traditional Doll & Hill criteria. 
     

    Faculty:  Maria Glymour

    Location:      Mission Hall 1406

    • Session Slides:

    • Lecture 1 Slides File

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

    • Watch URL

    • Required Reading

       Winship C, Morgan SL. The estimation of causal effects from observational data. Annual Review of Sociology. 1999;25:659-706.

      ONLY NEED TO READ PAGES 659-669: This is an excellent paper but takes a lot of work to get through. 

    • Cook T, Shadish W, Wong V. Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons. Journal of Policy Analysis and Management. 2008;27(4):724-750. File
      Not available unless: Your ID number contains 02
    • Winship C, Morgan SL. The estimation of causal effects from observational data. Annual Review of Sociology. 1999;25:659-706. File
      Not available unless: Your ID number contains 02

    • Optional Reading:

        Cook T, Campbell D, Shadish W. Experimental and quasi-experimental designs for generalized causal inference: Houghton Mifflin; 2002. chapter 2, pg 37-

         

         

      1. Assignment: Optional narrative description

         

    • Monday, April 4, 2016; 10:30 AM - 12:30 PM

      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
        Not available unless: Your ID number contains 02
      • Lee File
        Not available unless: Your ID number contains 02
      • Singer File
        Not available unless: Your ID number contains 02
      • Greenland File
        Not available unless: Your ID number contains 02

      • 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

    • Monday, April 11, 2016; 10:30 AM - 12:30 PM

      Lecture:  Longitudinal study designs: data sources

       The goal of this lecture is to consider how alternative data sources have different strengths and weaknesses that make them (in)appropriate for a research question. It is also to familiarize you with some major categories of study design.

      Faculty:  Maria Glymour

      Location:        Mission Hall 1406

      • Session Slides:

      • slides for lecture 3 File

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

      • Watch URL

      • Required Reading:

        1. Wu T. The Atherosclerosis Risk in Communities (ARIC) Study: Design and Objectives. American Journal of Epidemiology. 1989;129(4):687.
        2. Pagidipati and Gaziano. Estimating deaths from cardiovascular disease: a review of global methodologies of mortality measurement.  Circulation. 2013; 127:749.
        3. Pickett KE, Luo Y, Lauderdale DS. Widening social inequalities in risk for sudden infant death syndrome. American Journal of Public Health 2005;95(11): 1976.
        4. Stroud N, Mazwi TML, Case LD, et al. Prestroke physical activity and early functional status after stroke. Journal of Neurology, Neurosurgery & Psychiatry 2009;80(9): 1019.
        5. Lee S, Colditz GA, Berkman LF, Kawachi I. Caregiving and risk of coronary heart disease in US women* 1:: A prospective study. American Journal of Preventive Medicine 2003;24(2): 113-9.
        6. Hauser R, Willis R. Survey design and methodology in the Health and Retirement Study and the Wisconsin Longitudinal Study. Population and Development Review. 2004;30:209-235.  (this is a long article – skim it for key features of study design)
        7. Banks J, et al. Disease and Disadvantage in the United States and England. JAMA 2006; 295 (2037)

      •  

      • Pagidipati-749-56 File
        Not available unless: Your ID number contains 02
      • ARIC Designv2 File
        Not available unless: Your ID number contains 02
      • Pickett Inequalities File
        Not available unless: Your ID number contains 02
      • Hauser DesignofHRSandWLS File
        Not available unless: Your ID number contains 02
      • Stroud PreStrokePhysAx File
        Not available unless: Your ID number contains 02
      • Banks DiseaseUSEngland2010 File
        Not available unless: Your ID number contains 02
      • Lee CaregivingCHD File
        Not available unless: Your ID number contains 02

      • Assignment: 

        There are a lot of readings here, but most are quite short. Throughout, the goal is to understand the research design, not the specific content of the study.  Prior to class, please post on the course website answers to the following questions:

        >Choose at least 3 distinct data sources (e.g., ARIC, HRS, death certificate data, NHS, etc), and give an example of a research question (e.g., a hypothesis about the effect of a specific exposure ona  specific outcome) you consider the study exceptionally strong to address.   For each, provide an example of a research question you consider the design very weak to address.  Explain why the data source is strong or weak for each question.  Do not just discuss the questions addressed in the readings, think of new questions, preferably things you might be interested in.  This is not supposed to be a commentary related to the substantive questions in the readings: the goal is to focus on the pros and cons of various data sources. For hypotheses each study would not be well equipped to address, if possible describe another study that could address the hypothesis.

    • Monday, April 18, 2016; 10:30 AM - 12:30 PM

      Lecture: Clustered Data: Person level clustering
       This lecture will continue the clustered data discussion from class 2, but instead of focusing on clustering due to spatial autocorrelation, we will discuss clustering due to repeated measures on the same person. This lays the foundation for growth curve analyses of longitudinal change over time. 

      Faculty: Maria Glymour

      Location: Mission Hall 1406

      • 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
        Not available unless: Your ID number contains 02
      • Hubbard GEE File
        Not available unless: Your ID number contains 02
      • 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
      • Optional reading URL
      • lecture 4 File
      • Lecture 4 in class quiz File

    • Monday, April 25, 2016; 10:30 AM - 12:30 PM

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


      Faculty:  Maria Glymour

      Location:        Mission Hall 1406

      • 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: Optional narrative description

    • Monday, May 2, 2016; 10:30 AM - 12:30 PM

      Lecture:Nontraditional Design and Analysis Approaches

      Difference in difference, IV, and regression discontinuity methods
       
      Faculty:  Maria Glymour

      Location:  Mission Hall 1406

      • Session Slides:

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

      • Watch URL

      • Required Reading:

        1. Glymour MM, Walter S, Tchetgen Tchetgen E.  Instrumental Variables in Social Epidemiology.  Forthcoming in: Methods in Social Epidemiology, 2nd edn, Oakes and Kaufman.
        2. Shadish W, Cook T. The renaissance of field experimentation in evaluating interventions. Annual review of psychology. 2009;60:607-629.ONLY NEED TO READ THROUGH PAGE 615 (ON EXPERIMENTAL DESIGNS)
        3. Brookhart et al., Instrumental variable methods in comparative safety and effectiveness research.  Pharmacoepidemiology and drug safety 2010 (19): 537-554
        4. Ludwig et al., Neighborhoods, Obesity, and Diabetes -- a randomized social experiment. NEJM 2011; 365:1509-19
        5. Glymour, Tchetgen Tchetgen, and Robins.  Credible Mendelian Randomization. Am J Epi.

      • Brookhart IV CompEffective2010 File
        Not available unless: Your ID number contains 02
      • Banks Mazzona 2012-1 File
        Not available unless: Your ID number contains 02
      • Glymour CredibleMR File
        Not available unless: Your ID number contains 02
      • Ludwig NbhdsObesityDiabetes File
        Not available unless: Your ID number contains 02
      • Shadish Cook File
        Not available unless: Your ID number contains 02

      • Optional Reading:

        Three optional articles you may find useful someday:

        1. Murray, D., S. Varnell, et al. (2004). "Design and analysis of group-randomized trials: a review of recent methodological developments." American Journal of Public Health 94(3): 423.
        2.        Hayes R, Bennett S. Simple sample size calculation for cluster-randomized trials. Intl J Epidemiol. 1999;28(2):319.
        3. Banks and Mazzonna. The effect of education on old age cognitive abilities: evidence from a regression discontinuity design.  The economic journal, 2012, vol 122 pg 418-488

        I also highly recommend Angrist & Pischke "Mostly Harmless Econometrics: An empiricist's companion"

      • Assignment: Optional narrative description

    • Monday, May 9, 2016; 10:30 AM - 12:30 PM

      Lecture:  Selection Bias
       

      Faculty:  Dr ER Mayeda

      Location:  Mission Hall 1406

      • Session Slides:

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

      • Watch URL

      • Required Reading:

        1. Hernán, M.A. & Robins, J.M. (2016). Chapter 8: Selection bias. Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming.
        2. Howe, L.D., Tilling, K., Galobardes, B., & Lawlor, D.A. (2013). Loss to follow-up in cohort studies: bias in estimates of socioeconomic inequalities.Epidemiology, 24(1), 1-9.
        3. Lajous, M., Banack, H.R., Kaufman, J.S., & Hernán, M.A. (2015). Should patients with chronic disease be told to gain weight? The obesity paradox and selection bias. The American journal of medicine, 128(4), 334-336.

         

      • Required Readings on Selection Bias Folder

      • Optional Reading:

        1. Hernán M.A., Hernández-Díaz S, Robins J.M. A structural approach to selection bias. Epidemiology. 2004;15(5):615-625.
        2. Mayeda E.R., Tchetgen Tchetgen E.J., Power M.C., et al. A simulation platform to quantify survival bias: an application to research on determinants of cognitive decline. American Journal of Epidemiology. In press.
        3. Appendix for Howe et al. paper
      • Optional Readings Folder

      • Assignment: Optional narrative description

      • slides re selection bias File
      • IV slides revise File

    • Monday, May 16, 2016; 10:30 AM - 12:30 PM

      Lecture: Evaluating Uncertainty

      We consider confidence intervals, the limits of p-values, the intuition of bootstrapping to estimate confidence intervals, and estimating and interpreting subgroup effects. 

      Faculty:  Maria Glymour


      Location:        Mission Hall 1406  

      • Session Slides:

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

      • Watch URL

      • Required Reading:

        1. Sterne JAC, Smith GD. Sifting the evidence - what's wrong with significance tests? British Medical Journal. Jan 27 2001;322(7280):226-+.
        2.  Ioannidis JPA. Why most published research findings are false. PLoS Med. 2006;2(8):e124: 0696-0701
        3. Grunkemeier, G. and Y. Wu (2004). "Bootstrap resampling methods: something for nothing?" The Annals of thoracic surgery 77(4): 1142.
        4. Ertel et al., Frailty modifies effectiveness of psychosocial intervention in recovery from stroke. Clin Rehabil. 2007. 21:511.

      • Optional Reading:

      • Sterne on p-values File
        Not available unless: Your ID number contains 02
      • On (Ir)Reproducible Science File
        Not available unless: Your ID number contains 02
      • Introduction to the bootstrap File
        Not available unless: Your ID number contains 02
      • Ertel FIRST Secondary File
        Not available unless: Your ID number contains 02

      • Assignment: Optional narrative description

    • Monday, May 23; 10:30 AM - 12:30 PM

      Highlighted

      Lecture: Power and Publication Bias

      We will discuss some basics of statistical power and minimum detectable effect size. These issues link closely with publication bias.

      Faculty:  Maria Glymour

      Location:        Mission Hall 1106

      • Session Slides:

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

      • Watch URL

      • Required Reading:

        1. Whitley Introduction to power and sample size

        2. Normand Tutorial in Biostatistics: Meta-analysis

        3. Ahmed Publication bias in meta-analyses

      • Whitley Intro to Sample Size and Power File
      • Normand intro to meta analyses File
      • Ahmed Publication Bias File
      • Tilling CaptureRecapture File
        Not available unless: Your ID number contains 02
      • Slides for uncertainty lecture File