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BSTA 790
FA 2006
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CAUSAL INFERENCE IN BIOMEDICAL RESEARCH
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instructors :: course information :: programs :: class notes :: homeworks
Marshall Joffe
Associate Professor
Division of Biostatistics, CCEB
Course Director email: mjoffe@cceb.med.upenn.edu
Location:
Blockley Hall – Room 505
Time: Mondays and Wednesdays – 10:30am – 12:00pm
View the syllabus (PDF)
Introduction - 9/6/06
Potential Outcomes - 9/6/06
Nonparametric Bounds - 9/11/06
Simple Randomized Trials - 9/11/06
Propensity Scores - 10/9/06
Partial Identification of Causal Effects - 10/9/06
Sensitivity Analysis - 10/11/06
Graphical Models - 10/18/06
Instrumental Variables - 10/25/06
Non-Compliance - 11/6/06
Principal Stratification - 11/8/06
Time Varying Treatments - 11/27/06
Assignment 1 - 9/8/06
Dataset format 1
(dta)
Dataset
format 2 (sas)
Dataset format 3
(txt)
Assignment 2 - 9/14/06
Assignment 3 - 9/25/06
Poisox dataset (.zip file)
Assignment 4 - 10/9/06
Final Project - 10/18/06
Assignment 5 - 11/1/06
Assignment 5 dataset (.txt file)
Holland, P. Statistics in Causal Inference. JASA 1986; 81(396): 945-960. - link to JSTOR
Rosenbaum, P and Rubin, D. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70(1): 41-55. - link to JSTOR
Manski, CF. Nonparametric Bounds on Treatment Effects. AEA Papers and Proceedings 1990; 80: 319-323.
Greenland, S. Exchangeability, and Epidemiological Confounding. International Journal of Epidemiology, 15:412-418, 1986.
Joffe MM, Ten Have TR, Feldman HI, and Kimmel SE. Model Selection, Confounder Control, and Marginal Structural Models: Review and New Applications. The American Statistician 2004; 58(4): 272-279.
Rubin DB. Estimating causal effects from large data
sets using propensity scores. Annals of Internal Medicine, 1997; 127:757-763.
Hirano K, Imbens GW. Estimation of causal effcts
using propensity score weighting: an application to data on right heart
catheterization. Health Services and Outcomes Research Methodology, 2001;
2:259–278.
Lunceford, J.K. and Davidian,
M. Stratification and weighting via the
propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 2004;
23:2937–2960.
Rosenbaum, P.R.
Does a dose-reponse relationship reduce sensitivity to hidden bias? Biostatistics 2003;
4: 1–10. - 10/4/06
Pearl, J.
Causal diagrams for empirical research. Biometrika 1995;
82(4): 669–688. - 10/4/06
Greenland, S., Pearl, J., and Robins, J.M.
Causal diagrams for epidemiological research. Epidemiology 1999;
10: 37–48. - 10/4/06
Rosenbaum, P.R.
Covariance Adjustment in Randomized Experiments and Observational Studies. Statistical Science 2002;
17(3): 286–304. - 10/11/06
Angrist, J. and Imbens, G.
[Covariance Adjustment in Randomized Experiments and Observational Studies]: Comment. Statistical Science 2002;
17(3): 304–307. - 10/11/06
Robins, J.M.
[Covariance Adjustment in Randomized Experiments and Observational Studies]: Comment. Statistical Science 2002;
17(3): 309–321. - 10/11/06
Rosenbaum, P.R.
[Covariance Adjustment in Randomized Experiments and Observational Studies]: Rejoinder. Statistical Science 2002;
17(3): 321–327. - 10/11/06
Joffe, M.M. and Brensinger,
C. Weighting in instrumental variables
and G-estimation. Statistics in
Medicine, 2003; 22:1285-1303. - 11/1/06
Imbens, G.W. and Rubin, D.B. Bayesian inference for
causal effects in randomized experiments with noncompliance (in Bayesian
Methods). The Annals of
Statistics, 1997; 25:305-327.- 11/1/06
Angrist, J.D., Imbens, G.W. and
Rubin, D.B. Identification
of causal effects using instrumental variables (in Applications and Case
Studies). Journal of the American
Statistical Association, 1996; 91:444-455.- 11/1/06
J. Pearl. Direct and indirect effects. UCLA Cognitive Systems Laboratory, Technical
Report (R-273), June 2001.
In Proceedings of the Seventeenth Conference on Uncertainty in Artificial
Intelligence, San Francisco, CA: Morgan Kaufmann, 411-420, 2001.- 11/1/06
Joffe, M., and Colditz, G.A.
Restriction as a method for reducing bias in the estimation of direct
effects. Statistics in Medicine, 1998;
17:2233-2249. - 11/1/06
Brookhart, M.A., Wang, P.S., Solomon, D.H., and Schneeweiss, S.
Evaluating Short-Term Drug Effects Using a Physician-Specific Prescribing Preference as an Instrumental Variable. Epidemiology 2006; 17: 268-275. - 11/1/06
Martens, E.P., Pestman, W.R., de Boer, A., Belitser, S.V., and Klungel, O.H.
Instrumental Variables: Applications and Limitations. Epidemiology 2006; 17: 260-267. - 11/1/06
Hernán, M.A.and Robins, J.M.
Instruments for Causal Inference: An Epidemiologist's Dream? Epidemiology 2006; 17: 360-372. - 11/1/06
Frangakis, C.E.and Rubin, D.B.
Principal Stratification in Causal Inference Biometrics 2002; 58: 21-29. - 11/7/06
Joffe, M.M., Small, D., and Hsu, C-Y.
Defining and estimating intervention effects for groups that will develop an auxiliary outcome. Statistical Science 2007; in press. - 11/7/06
Hernán, M.A., Brumback, B.A., and Robins, J.M.
Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures. Statistics in Medicine 2002; 21: 1689-1709. - 11/27/06
Robins, J.
The control of confounding by intermediate variables Statistics in Medicine 1989; 8: 679-701. - 11/27/06
Robins, J.M., Blevins, D., Ritter, G., and Wulfson, M.
G-estimation of the effect of prophylaxis therapy for Pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology 1992; 3: 319-336. - 11/27/06