Lectures: Tuesday, Thursday, 10:30-12:00, Small Biostat Classroom
Office hours:
Date | Lecture | Topic | Reading | Slides | Homework | ||||||
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Jan 9 | Potential Outcomes; Rubin Causal Model | Imbens GW, Rubin DB (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press. Chapters 1, 3. | Lecture1 | ||||||||
Jan 11 | Rubin Causal Model | Holland PW (1986). Statistics and causal inference. JASA 81: 945-960. Imbens GW, Rubin DB (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press. Chapters 1, 3. Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press. Section 11.4.5. (Causation without Manipulation!!!) |
Lecture2 | ||||||||
Jan 16 | Causal Diagrams / Directed Acyclic Graphs | Greenland S, Pearl J, Robins JM (1999). Causal diagrams for epidemiologic research. Epidemiology 10: 37-48. Hernan MA, Hernandez-Diaz S, Robins JM (2004). A structural approach to selection bias. Epidemiology 15: 615-625. |
Lecture3a, Lecture3b | ||||||||
Jan 18 | Causal Diagrams and Identification of Causal Effects | Pearl J (1995). Causal diagrams for empirical research. Biometrika 82: 669-710. Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press. Chapter 3. |
Lecture4 | ||||||||
Jan 23 | Causal Diagrams and Identification of Causal Effects (continued) | Pearl J (1995). Causal diagrams for empirical research. Biometrika 82: 669-710. Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press. Chapter 3. |
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Jan 25 | Propensity Scores | Joffe MM, Rosenbaum PR (1999). Invited commentary: propensity scores. American Journal of Epidemiology 150: 327-333. Rosenbaum PR, Rubin DB (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41-55. Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press. Section 11.3.5. (Understanding Propensity Scores) |
Lecture5a-RR1983, Lecture5b-JR-1999 | Several DAGS (due Jan 30) | |||||||
Jan 30 | Propensity Scores in Practice | Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K, Robins JM (2005). Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. American Journal of Epidemiology 163: 262-270. Franklin JM, Eddings W, Austin PC, Stuart EA, Schneeweiss S (2017). Comparing the performance of propensity score methods in healthcare database studies with rare outcomes. Statistics in Medicine. Austin PC, Stuart EA (2015). Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in Medicine 34: 3661-3679. |
Lecture6a-KWG2005, Lecture6b-IPTW-standardizing-collapsibility | ||||||||
Feb 1 | Propensity Scores in Practice (continued) | Lecture7a-AS-2015, Lecture 7b-FEA2017, Lecture7c-PS-continuous.pptx | |||||||||
Feb 6 | Double Robustness | Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005;61:962972. [sections 1-2,5-6] Kang JDY, Schafer JL. Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science 2007; 22:523-539. |
Lecture8 | Apply 4 different PS approaches and a standard regression-based approach to estimate the causal effect of starting an NNRTI-based regimen vs. a PI-based regimen on time from ART initiation to death in simulated CCASAnet data. Obtain a doubly robust estimator for the homework described above. Perform a sensitivity analysis to investigate the sensitivity of the association seen in the earlier CCASAnet analysis on unmeasured confounding. Due Feb 20. |
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Feb 8 | BRYAN OUT OF TOWN | Guest Lecture by Lucy D'Agostino McGowan | Lucy's Slides: |
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Feb 13 | Sensitivity Analyses | ! VanderWeele TJ, Ding P (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine. Ding P, VanderWeele TJ (2016). Sensitivity analysis without assumptions. Epidemiology 2016; 27: 368-377. |
Lecture9 | ||||||||
Feb 15 | Mediation Analyses | VanderWeele TJ (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Chapter 2, sections 2.1-2.6, 2.16, 2.10-2.15, 2.17-2.19. Appendix, sections A.2.1- A.2.2. | Lecture10 | ||||||||
Feb 20 | Mediation Analyses | VanderWeele TJ (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Chapter 3, sections 3.2-3.4; Chapter 5, section 5.1. | With the JOBS II dataset, perform a mediation analysis estimating the controlled direct effect, the natural direct effect, and the natural indirect effect of job training on (a) depressive symptoms considering job_seek as a mediating variable, and on (b) employment status considering the mediating variable job_seek. The JOBS II dataset can be found at https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/14801. This contains the data and analyses for Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychological Methods 2010; 15: 309-334. Code up the analyses suggested in Chapter 2 of VanderWeele, but compare and contrast these estimates with those obtained using the approach of Imai and colleagues. Due March 1. |
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Feb 22 | Compliance | Angrist JD, Imbens GW, Rubin DB (1996). Identification of causal effects using instrumental variables. JASA 91:444-455 | Lecture11 | ||||||||
Feb 27 | Instrumental Variables | Baiocchi M, Cheng J, Small DS (2014). Tutorial in biostatistics: instrumental variable methods for causal inference. Statistics in Medicine 33: 2297-2340. Sections 1-4, 5.1-5.2, 6-8, and 13-14. | Lecture12 | The dataset listed below contains results from a simulated randomized trial. Participants were assigned to treatment or control (assign) and their CD4 count was measured after 3 months of follow-up (cd4). The treatment they actually took is also recorded (trt), as is a baseline measure of the patients health status (health.status). Estimate the effect of assignment to treatment on CD4 (ITT effect), and the compliers average causal effect (CACE or LATE). Describe the assumptions made for estimation and contrast these estimates with more naive estimates such as the per protocol estimate and the as-treated estimate. Due March 13. |
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Mar 1 | Principal Stratification | Frangakis CE, Rubin DB (2002). Principal stratification in causal inference. Biometrics 58: 21-29. Gilbert PB, Bosch RJ, Hudgens MG (2003). Sensitivity analysis for the assessment of causal vaccine effects on viral loads in HIV vaccine trials. Biometrics 59; 531-541. Shepherd BE, Gilbert PB, Mehrotra DV (2007). Eliciting a counterfactual sensitivity parameter. The American Statistician 61: 1-8. |
Lecture13a, Lecture13b | ||||||||
Mar 6 | SPRING BREAK | ||||||||||
Mar 8 | SPRING BREAK | ||||||||||
Mar 13 | Principal Stratification Goal or Tool? | Pearl J (2011). Principal stratification a goal or a tool? International Journal of Biostatistics 7: 20. !VanderWeele TJ (2011). Principal stratification uses and limitations. International Journal of Biostatistics 7:28. Joffe MM (2011). Principal stratification and attribution prohibition: good ideas taken too far. International Journal of Biostatistics 7: 35. Sjolander A (2011). Reaction to Pearls critique of principal stratification. International Journal of Biostatistics 7: 22. |
Lecture14 | A clinical trial was performed to evaluate the effect of an intervention (spontaneous breathing treatment in the ICU) on cognitive function. Cognitive function is only measured on survivors. 1) Perform an analysis comparing cognitive function among survivors; what problems does this analysis have that make it hard to interpret this causally? 2) Perform an intention-to-treat type analysis where those who die are assigned poor cognitive function; what problems does this analysis have? 3) Perform a principal stratification analysis estimating the causal effect of intervention on cognitive function among those who would have survived to 3 months regardless of treatment assignment. Perform a sensitivity analysis under under SUTVA, randomization, and monotonicity; derive large sample bounds and estimate with a sensitivity parameter. Data will be emailed to you and are not to be shared or posted. Due April 3 |
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Mar 15 | Econometric Causality | Heckman JJ (2008). Econometric causality. International Statistical Review 76: 1-27. Moscoe E, Bor J, Barnighausen T (2015). Regression discontinuity designs are underutilized in medicine, epidemiology, and public health: a review of current and best practice. Journal of Clinical Epidemiology 2015; 68: 132-143. |
Lecture15a, Lecture15b | ||||||||
Mar 20 | Time-varying confounding | Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC (2012). Methods for dealing with time-dependent confounding. Statistics in Medicine 32: 1584-1618. Sections 1-4. | Reproduce Simple Example g-computation and IPW of MSM in Section 4; Reproduce simulations of section 5. Due April 3. | ||||||||
Mar 22 | BRYAN OUT OF TOWN Lesser-known estimands: ATM and ATO |
Guest Lecture by Laurie Samuels Li, L., & Greene, T. (2013). A weighting analogue to pair matching in propensity score analysis. The International Journal of Biostatistics, 9(2), 215234. Li, F., Morgan, K. L., & Zaslavsky, A. M. (2017). Balancing Covariates via Propensity Score Weighting. Journal of the American Statistical Association, 0(0), 111. (As of 16 Feb 2018, this one is available online, but has not been assigned to an issue.) http://www.tandfonline.com/doi/full/10.1080/01621459.2016.1260466 |
Laurie-Slides | ||||||||
Mar 27 | BRYAN OUT OF TOWN | ||||||||||
Mar 29 | BRYAN OUT OF TOWN | ||||||||||
Apr 3 | Marginal structural models | Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC (2012). Methods for dealing with time-dependent confounding. Statistics in Medicine 32: 1584-1618. Sections 5, 7-9. | |||||||||
Apr 5 | Marginal structural models | Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC (2012). Methods for dealing with time-dependent confounding. Statistics in Medicine 32: 1584-1618. Sections 5, 7-9. | Lecture16; simulation-code | ||||||||
Apr 10 | MSM | Robins JM, Hernan MA, Brumback B (2000). Marginal structural models and causal inference in epidemiology. Epidemiology 11: 550-560. Hernan MA, Brumback B, Robins JM (2000). Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11: 561-570. |
Lecture17 | Perform a marginal structural model analysis. Estimate the causal effect of HAART on mortality using the simulated HIV data attached below. Your analysis should be similar to that of Hernan, Brumback, and Robins (2000). More details are in the attached document. Due April 24. msm-assignment-instructions Simulated-HIV-data.zip | |||||||
Apr 12 | Dynamic Marginal Structural Models | Hernan MA, Lanoy E, Costagliola D, Robins JM (2006). Comparison of dynamic treatment regimes via inverse probability weighting. Basic and Clinical Pharmacology and Toxicology 98: 237-242. Cain LE, Robins JM, Lanoy E, Logan R, Costagliola D, Hernan MA (2010). When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data. International Journal of Biostatistics 6: 18. |
Lecture18 | ||||||||
Apr 17 | Applications of Dynamic Marginal Structural Models | Shepherd BE, Jenkins CA, Rebeiro PF, Stinnette SE, Bebawy SS, McGowan CC, Hulgan T, Sterling TR (2010). Estimating the optimal CD4 count for HIV-infected persons to start antiretroviral therapy. Epidemiology 21: 698-705. Shepherd BE, Liu Q, Mercaldo N, Jenkins CA, Lau B, Cole SR, Saag MS, Sterling TR (2016). Comparing results from multiple imputation and dynamic marginal structural models for estimating when to start antiretroviral therapy. Statistics in Medicine 35: 4335-4351. |
Lecture19 | ||||||||
Apr 19 | BRYAN OUT OF TOWN | Guest Lecture by Christina Tripp | |||||||||
Apr 24 | Targeted maximum likelihood estimation | Schuler MS, Rose S (2017). Targeted maximum likelihood estimation for causal inference in observational studies. American Journal of Epidemiology 185: 65-73. | replicating-Schuler-Rose-simulations.R | ||||||||
Apr 26 | Causal inference never a dull topic | Dawid P (2000). Causal inference without counterfactuals, with Discussion. JASA 95: 407-448. Hernan MA (2018). The C-word; scientific euphimisms do not improve causal inference from observational data. AJPH; 108: 616-619, with discussion. |