Section outline

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  • Friday, April 5, 2019; 2:00 PM - 4:00 PM

    Class 1: 

    a) Causal Inference in the Context of Observational Data: identifying threats to validity and integrating alternative frameworks

    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. 
     

    b) Introduction to Representative Sampling

    We will introduce representative sampling, pros and cons of simple random samples, stratified sampling, and clustered sampling. This lays the groundwork for discussion of analyses of clustered data in the coming weeks. 

    Faculty:  Maria Glymour

    Location:      Rock Hall 102

    • In Class Quiz: Please try to complete the quiz prior to the lecture.  Only spend about 10 minutes on it.  

    • Session Slides:

    • Lecture 1 Slides File

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

    • Watch URL


      Required Reading 

      Skim 2 chapters of Shadish Cook and Campbell: 

      Cook T, Campbell D, Shadish W. Experimental and quasi-experimental designs for generalized causal inference: Houghton Mifflin; 2002. chapter 2, Statistical conclusion validity and internal validity pg 33-63

      Cook T, Campbell D, Shadish W. Experimental and quasi-experimental designs for generalized causal inference: Houghton Mifflin; 2002. chapter 3, Construct validity and external validity pg 64-102

      Read Korn intro to sampling:

      Korn EL, Graubard BI. Epidemiologic studies utilizing surveys: accounting for the sampling design. Am J Public Health. 1991;81(9):1166–1173.

      Read a section of Winship

       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. 

    • Shadish Cook and Campbell, Chapter 2 File
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    • Shadish Cook and Campbell, chapter 3 File
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    • Winship reading File
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    • Korn reading on sampling File
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    • Optional Reading:

         

         

      1. 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

      2. Assignment/Reading Response: Please post to the forum:

        1) Provide an example of 4 threats to validity that you have encountered in your research, drawing one from each of the domains Cook and Campbell delineate (statistical conclusion validity, internal validity, construct validity, and external validity).

        2) For any data set you frequently use, look up the sample design and describe it. 

         

      3. Welcome Quiz File

    • Friday, April 12, 2019; 2:00 PM - 4:00 PM

      Lecture:  Clustered Data Arising from Cluster Randomized Trials and Geographically Clustered Observational Data
       This lecture will discuss the challenges and statistical approaches to address clustered data.

      Faculty:  Catie Oldenburg

      Location:        Rock Hall 102

      • Session Slides:

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

      • Watch URL

      • Required Reading:

        Start with the Murray and Wawer Readings regarding cluster randomized trials.  They were recommended by Dr. Oldenburg.  The other four papers skim for now; they will be very relevant as we move into more about multilevel modeling.  

        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.
        5. Murray et al (2018)  Design and analysis of group-randomized trials in cancer: A review of current practices. Preventive Med 111. Pg. 241.
        6. Wawer et al (1999) Control of sexually transmitted diseases for AIDS prevention in Uganda: a randomised community trial 353. pg 525.

        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. 

      • Murray File
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      • Wawer File
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      • 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).

        Additional Assignment for Data Application IS: 

        1) Register to use data at: https://usa.ipums.org/usa/.  

        2) Download a data set from the 2000 Census 5% sample, including at a minimum basic demographics, year of birth, education, state (FIPS), and all available disability variables.  Feel free to include anything else you'd like.  You can also restrict by age or another variable to make the data set smaller (use sample case selection). 

        3) Open the data set in the statistical software of your choice.  I suggest that you write your code to pull a 1% sample of the data, so you can manipulate it.  There are many ways to complete this selection, for example generate a random number with a uniform distribution and keep only those observation with random number <.01.  You may find that you cannot open the data set at all on your computer.  If so, go back to the IPUMS data set and select "customize sample sizes" and change the requested density.  

        4) Create a variable that is the % of people with <=6 years of education in each state (state low education).  

        5) Estimate a linear regression (not accounting for clustering by state) using education and state low education predicting self-care disability. 

        6) Estimate a mixed model with random intercepts for state and no fixed effects.  

        7)  Estimate a mixed model with random intercepts for state and own education as a fixed effect.  

        8)  Estimate a mixed model with random intercepts for state, own education as a fixed effect, and state low education as a fixed effect.

        9)  Next week we will ask you to write a summary of what you have done.  For this week, just try it and figure out all of the problems you will have getting the data, handling the data, specifying and interpreting the models. 

      • Quiz for Lecture 2 File
      • Lecture 2 Slides for 2019 File

    • Friday, April 19, 2019; 2:00 PM - 4:00 PM

      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
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      • 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

    • Friday, April 26, 2019; 2:00 PM - 4:00 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:        Rock Hall 102

      • Session Slides:

      • slides for lecture 4 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
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      • ARIC Designv2 File
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      • Pickett Inequalities File
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      • Hauser DesignofHRSandWLS File
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      • Stroud PreStrokePhysAx File
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      • Banks DiseaseUSEngland2010 File
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      • 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.

        Extra Assignment for the Applied Data IS:

        Write a summary of the methods and results of your project in the 2000 Census last week. Include a Table 1 describing the data and a Table 2 summarizing the results of your regression models.

    • Friday, May 3, 2019; 2:00 PM - 4:00 PM

      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
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      • Mishra methods lifecourse File
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      • Fitzpatrick ObesityDementia File
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      • Willis LIfecourseBP File
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      • Ben Shlomo Kuh Lifecourse File
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      • 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

    • Friday, May 10, 2019; 2:00 PM - 4:00 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:        Rock Hall 102

      • 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. Borenstein, Michael; Hedges, Larry V; Higgins, Julian P.T; A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Research Synthesis Methods, 04/2010, Volume 1, Issue 2
        5. Ertel et al., Frailty modifies effectiveness of psychosocial intervention in recovery from stroke. Clin Rehabil. 2007. 21:511.

      • Required Articles:

      • Sterne on p-values File
        Not available unless: Your ID number contains 02
      • On (Ir)Reproducible Science File
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      • Introduction to the bootstrap File
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      • Borenstein, Hedges, et al. Intro to Meta Analysis File
        Not available unless: Your ID number contains 02
      • Ertel FIRST Secondary File
        Not available unless: Your ID number contains 02

      • Assignment: 

        Specify a hypothesis regarding a particular exposure and outcome and a binary effect modifier including specific measures of association (specify the magnitudes of that association you anticipate: I suggest making everything cross-sectional). Using the software of your choice, generate a population with 1000 people under a causal structure consistent with this hypothesis. Draw a simple random sample 100 individuals from this population and estimate the population average exposure-outcome association and the association stratified by your modifier of interest within this subset.  Repeat this 10 times and write the parameter estimates and CI each time.

        Repeat the data set construction, setting the causal effect to the null.  Again repeat this 10 times and write the parameter estimate and CI each time (if you figure out how to automate it, run it 1000 times and post the histogram of the parameter estimates and p-values).

        Use your code above and also a canned software command to estimate statistical power to detect the difference in means under the settings below:

        *n=100, μ0=.02, μ1=.12, SD=1, α=.05

        *n=100,μ0=.02, μ1=.12, SD=2, α=.05

        *n=500, μ0=.3, μ1=.3, SD=1, α=.05

         For each of the 3 settings above, what is the power to detect whether the ratio of the means=1?

      • Uncertainty Slides File

    • Friday, May 17, 2019; 2:00 PM - 4:00 PM

      Mediation and Effect Decomposition
       

      Faculty:  

      Location:        Rock Hall 102

      • Watch URL
      • Required Reading: VanderWeele File
      • Required Reading: Judd File
      • Required Reading: Nandi File
      • Required Reading: Kim and VanderWeele Applied Example File
      • Required Reading: Nguyen IORW File
      • Required Reading: Rudolph File

      • Required Reading:

        There has been a lot of work on mediation in the past 20 years, much of it published by Tyler VanderWeele.  His Annual Review paper is the key reading for this week and next.  Don't get lost in the formulas: they all come down to the same idea.  Once you understand the linear setting, you can wade through the algebra and realize the formulas for non-linear models are doing the same thing.  Focus on understanding the counterfactual contrasts that we refer to as direct or indirect effects and the assumptions you need to estimate them.

        The applied examples are just to show you conventional approaches and more modern examples (I've tried to order roughly in order of the development of the field, from the most conventional approach as with Judd to still-developing approaches such as Rudolph).  Consider these articles spread across this week and next week (May 17 and May 24).      

        1. VanderWeele TJ. Mediation Analysis: A Practitioner's Guide. Annual Review of Public Health 2016.

        Applied paper examples

        1. Judd et al., Dietary Patterns Are Associated With Incident Stroke and Contribute to Excess Risk of Stroke in Black Americans

        2. Nandi, Glymour, Kawachi, and VanderWeele. Using Marginal Structural Models to Estimate the Direct Effect of Adverse Childhood Social Conditions on Onset of Heart Disease, Diabetes, and Stroke. Epidemiology 2012. 

        3. Kim ES, VanderWeele TJ. Mediators of the association between religious service attendance and mortality. American journal of epidemiology. 2018 Sep 27;188(1):96-101.
        4. Nguyen TT, Tchetgen EJ, Kawachi I, Gilman SE, Walter S, Glymour MM. The role of literacy in the association between educational attainment and depressive symptoms. SSM-population health. 2017 Dec 1;3:586-93.

        5. Rudolph KE, Sofrygin O, Schmidt NM, Crowder R, Glymour MM, Ahern J, Osypuk TL. Mediation of neighborhood effects on adolescent substance use by the school and peer environments. Epidemiology. 2018 Jul 1;29(4):590-8.

      • Optional Reading:

      • Assignment:

        Please identify a quantitative research article evaluating mediation in your field and provide the citation.

        What is the primary discipline of the authors?

        Draw a DAG representing the implicit or explicit causal model explored in this paper (you do not need to post your DAG, but we will try to discuss in class).

        What is the exposure of interest?

        What is the outcome of interest?

        What is the hypothesized mediator of interest and how is it measured?

        Describe the modeling approach and briefly report the estimated total, direct, and indirect effects (if these are reported). 

        If the direct effect is reported, would you describe this as a natural direct effect, a controlled direct effect, or something else?

        Do you think there is potential measurement error in the mediator and how would that affect the results?

        Do you think there are unmeasured confounders of the mediator-outcome association and how would that affect the results of the mediation analysis?

        Do you have any critiques of the paper? 

      • Mediation slides File

    • Friday, May 24, 2019; 2:00 PM - 4:00 PM

      Mediation and Effect Decomposition, part 2

       
      Faculty:  

      Location:  Rock Hall 102

      • Watch URL

      • Required Reading:

        1. Readings are the same as from last week re mediation.

      • Assignment: Optional narrative description

        Specify a hypothesis regarding a binary exposure, a continuous mediator, and a continuous outcome.  Specify how each variable affects its children (i.e., how the exposure influences the mediator) and the distribution of the random or unmeasured determinants of the child variables. 

        Using the software of your choice, generate a population with 10000 people under a causal structure consistent with this hypothesized causal structure. Use a conventional Baron-Kenny decomposition to estimate the direct and indirect effects of the exposure on the outcome.  

        Now introduce a confounder of the mediator and outcome (C) into your causal model.  Define the new causal models and simulate a new data set.  Use a conventional decomposition without control for the confounder first and then with control for the confounder to derive estimates of the direct and indirect effects of exposure on outcome. 

        Create a new version of the mediator that represents a badly measured version of that variable, for example by taking the original variable and adding some random noise to it.  Now use that mediator to evaluate the direct and indirect effects. 

        Now try estimating the CDE: 

        (1) make an identifier for your data (in stata, "gen id=_n")

        (2) make 3 copies of every observation (in stata, use "expand 3"); now you have 2 fake copies of each observation and one real copy.

        (3) for the first "fake" copy of each observation, set x to 0 and m to 0 and y to .  (missing)

        (4) for the second "fake" copy of each observation, set x to 1, m to 0 and y to . (missing)

        (5) estimate a regression model predicting the outcome as a function of exposure, mediator, the interaction of the exposure and mediator,  and the mediator-outcome confounder (C), using only the real observations.

        (6) for the first fake copy of each observation, use the predict statement to predict the counterfactual value of y,    setting x to 0 and m to 0

        (7) for the second fake copy of each observation, use the predict statement to predict the counterfactual value of y    setting x to 1 and m to 0

        (8) estimate the controlled direct effect of x on y, setting m to 0

        /* Bonus hw if you're having fun. 

        Go back to your original data (before you calculated the CDE

        (1) make an identifier for your data (in stata, "gen id=_n")

        (2) make 3 copies of every observation (in stata, use "expand 3"); now you have 2 fake copies of each observation and one real copy.

        (3) for the first "fake" copy of each observation, set x to 0 and m to . and y to . 

        (4) for the second "fake" copy of each observation, set x to 1, m to . and y to .

        (5) estimate a regression model predicting the mediator as a function of x and c, using only the real observations

        (6) predict cf_m_x0 in the first fake copy

        (7) predict cf_m_x1 in the second fake copy

        (8) estimate a regression model predicting the outcome as a function of exposure, mediator, the interaction of the exposure and mediator,  and the mediator-outcome confounder (C), usin gonly the real observation

        (9) in the first fake copy, set m to cf_m_x0 and set x to 1

        (10) in the second fake copy, set m to cf_m_x1 

        (11) in the first fake copy,  predict y based on the regression model in (8), to estimate cf_y_x1_cf_m_x0

        (12) in the second fake copy, predict y based on the regression model in (8), to estimate cf_y_x1_cf_m_x1

        (13) estimate the natural indirect effect of x on y, mediated by m

        */

        If you need coding hints for any of the above, see the scribbles above under "HWMediation"


    • Friday, May 31, 2019; 2:00 PM - 4:00 PM

      Lecture: Instrumental Variables Analyses

      Faculty:  Maria Glymour


      Location:        Rock Hall 102

      • Session Slides:

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

      • Watch URL
      • AngristKrueger Applied IV File
        Not available unless: Your ID number contains 02
      • Glymour Walter Tchetgen Tchetgen Intro IV Chapter File
        Not available unless: Your ID number contains 02
      • Ludwig NbhdsObesityDiabetes File
        Not available unless: Your ID number contains 02
      • Brookhart IV CompEffective2010 File
        Not available unless: Your ID number contains 02

      • Required Reading:

        Required reading:
        • Angrist and Krueger.  Beyond supply and demand.  This is a friendly (for an economist) introduction to using IV to address confounding bias.  The best part is the examples (Table 1!).
        • Brookhart.  Comparative Effectiveness IVs.  Nice examples of using IVs in diverse health research settings.
        • Ludwig.  IV applied to an actual trial (Moving to Opportunity).  Very intuitive setting.
        Optional reading:
        • Glymour Walter, Tchetgen Tchetgen. Methods in Social Epi chapter 19.  This is a long chapter so you may not want to read it all, but it summarizes most of what Maria has to say on the topic

      • Assignment: 

        Find an RCT of interest to you.   Describe:
        1) What was the exposure and outcome being evaluated?
        2) What was the adherence to randomly assigned treatment (and how was it measured)?
        3) What was the primary intent-to-treat effect estimate?  
        4) Did they report an IV effect estimate?  
        5) Would an IV effect estimate have been of interest in this study?  
        6) If so, do you think the IV estimate would be of more interest than the ITT estimate?  Why/why not?
        7) Can you calculate the IV effect estimate based on the information provided?  If so, what is it?  If not, why not?

      • Session Slides on IV File

    • Friday, June 6, 2019; 2:00 PM - 4:00 PM

      Lecture:  Review and Mop-Up
       

      Faculty:  Maria Glymour

      Location:  Rock Hall 102

      • Session Slides:

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

      • Watch URL

      • No Required Reading. Some fun articles:

        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.
        4. Whitley Introduction to power and sample size
        5. Normand Tutorial in Biostatistics: Meta-analysis

        6. Ahmed Publication bias in meta-analyses

         

      • Required Readings on Selection Bias Folder
      • Greenland BiasAnalysisHandbookofEpi File
      • Normand intro to meta analyses File
        Not available unless: Your ID number contains 02
      • Ahmed Publication Bias File
        Not available unless: Your ID number contains 02
      • Tilling CaptureRecapture File
        Not available unless: Your ID number contains 02
      • Whitley Intro to Sample Size and Power File
        Not available unless: Your ID number contains 02
      • Greenland 2012.Causal inference as a prediction problem.In Berzuini et al.corr File
      • Lash BiasAnalysis Int. J. Epidemiol.-2014-Lash-1969-85 File

      • 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
      • review slides File