Peter badgio, RESEARCH ASSOC



STA542: Statistical Methods for Observational Studies

Required Textbooks:

Analysis of Observational Health Care Data Using SAS – 2010

Causal Inference Through Potential Outcomes – D.B. Rubin, E. Stuart, and S. Cook

Faries, D.E., Leon, A.C., Haro, J.M., Obenchain, R.L. (2010). Analysis of Observational Health Care Data Using SAS, Cary, NC: SAS Institute Inc. ISBN 978-1-60764-227-5.

Rubin, D.B., Stuart, E., and Cool, S. (2003). Basic Concepts of Statistical Inference in Experiments and Observational Studies, Harvard University.

Optional Book: P. Rosenbaum (2002), Observational Studies, 2ndEdition, New York:Springer- Verlag.

Students must purchase the Faries et al. (2010) book. Students will be provided the Rubin et al. (2003) materials.

GOALS of STA 542: Students completing this course should

• Be competent on the analysis of randomized trials per the content presented in STA512.

• Be competent in regression modeling building and diagnostics

• Understand the difference between Experimental designs versus Observational designs.

• Be introduced to observational modeling techniques such as propensity score techniques, instrumental variables, and potential outcomes.

• Introduced on the Rubin Causal Model,

• Competence with SAS Procedures PROC GLM, PROC REG, PROC QLIM, PROC SYSLIN, and PROC IML.

TECHNOLOGY: Students will be using SAS 9.4 Students should be competent in using SAS prior to taking this course. The following skills are expected to be known:

• Reading and creating SAS data sets.

• Familiarity with PROC REG and PROC GLM introduced in STAT 512.

• Able to save output files, SAS code, and SAS logs

• Install SAS on their personal computer.

• Comfortable in the SAS lab.

Student Learning Objectives:

1.         Demonstrated an understanding of randomization, imbalance, and confounding.

2.         Demonstrated the ability to apply the elementary methods of statistical analysis, namely those based on the regression linear models ideas to perform data analysis for the purposes of statistical inference.

3.         Demonstrated proficiency in the effective use of computers for research data management and for analysis of data with standard statistical software packages, particularly SAS.

4.           Learned to develop and critically assess design of observational studies and the collection of data.

5.         Applied one or more methods of statistical inference to a particular area of interest, particularly the program in the elective concentration.

6.         Gained practical experience in statistical consulting and communicating with non-

statisticians, culminating with interaction with research workers at a local company as part of the internship practicum.

Course Learning Outcomes: Students will be able to:

1. Determine the correct statistical analysis for a given set of data [SLO1,SLO2, SLO4]

2. Utilize statistical software to analyze linear models and correctly interpret the output. [SLO2, SLO3]

3. Utilize statistical software to perform logistic regression for PROPENSITY SCORE models and correctly interpret the output. [SLO2, SLO3,SLO4]

4. Utilize statistical software to analyze OBSERVATIONAL OUTCOME using PROPENSITY SCORES as covariates, weights, or matched samples as well as correctly interpret the output. [SLO2, SLO3,SLO4]

5. Utilize statistical software to analyze OBSERVATIONAL OUTCOMES models, though INSTUMENTAL VARIABLES, and correctly interpret the output. [SLO2, SLO3,SLO4]

6. Utilize statistical software to perform PROPENSITY SCORE MODELS for OBSERVATIONAL models with more than TWO GROUPS through covariate adjustments. [SLO2, SLO3,SLO4]

7. Discuss goodness-of-fit techniques for PROPENSITY SCORE MODEL AND INSTURMENTAL VARIABLES. [SLO2, SLO3,SLO4].

8. INTRODUCTION TO RUBIN CAUSAL MODEL, STRUCTUAL NESTED MEAN MODEL, and POTENTIAL OUTCOMES FRAMEWORK. [SLO2, SLO3,SLO4]

9. Communicate the results of these statistical analyses in a concise, simple way that would be understandable to a non-statistician. [SLO2, SLO4, SLO5, SLO6]

EXAMS: The first exam is xxx. The second exam is yyy.

EVALUATION COMPONENTS: Two in class tests at 25% each (TBA & TBA) and the final exam at 30%, individual project at 20%.

Evaluation: Exam 1 (in-class) [CLO1-CLO4, CLO7] 25%

Exam 2 ( in-class) [CLO1- CLO7] 25%

Final Exam (in-class) [CLO1-CLO9] 30%

Final Project [CLO9] 20%

ATTENDANCE: Attendance is important and expected. Absence from a test is acceptable for illness/emergency/official University business. Please contact me ASAP by e-mail or phone. Written verification may be required.

DISHONESTY: Any instance of dishonesty will be dealt with according to University policy.

DISABILITIES: We at West Chester University wish to make accommodations for persons with disabilities. Please make your needs known to me and to the Office of Services for Students with Disabilities (3217). Sufficient notice is needed in order to make accommodations possible.

WITHDRAWAL:

TOPICS: We will follow the Faries et al. (2010) book then follow the Rubin et al. (2003) materials.

HOMEWORK

All Homework will be assigned at the end of each class and from the textbook. Homework is DUE. Subsequent week we will review my final SAS code and the relevant SAS output. I recommend you have your syntax & output available.

PROJECT

The project is worth 20% of your grade.

The project should include the following sections:

1. Background. Give a short description of the problem and its significance.

2. Data. Describe the variables and give the number of cases. Indicate any special characteristics concerning the experimental design.

3. Model. Explain the statistical model used.

4. Results. Describe the results . You can include short tables and graphical displays here.

5. Conclusions. State the conclusions in terms of the context of the background information that you provided in the first section. Be concise and avoid technical jargon.

Getting started.

• Find a research article that had Observational data. You will read this article

• You must highlight their analytical techniques used for the collected Observational techniques

• Be critical of the techniques presented. You critique can be either positive or negative. Did the used techniques properly address issues with the collected observational data.

• Present their findings. Discuss limitations, flawed interpretations, and potential insights.

• Discuss what you would do if a subsequent followup study was to be conducted.

• Your summary should be between 5-10 pages (double spaced, one-sided per page) electronically submitted via the Assignments/Dropbox folder on D2L.

• Think of what you are submitting is a review article. Therefore, your paper should consist of an Abstract, Introduction, Methods, Results, and Conclusions.

ACADEMIC & PERSONAL INTEGRITY

It is the responsibility of each student to adhere to the university’s standards for academic integrity. Violations of academic integrity include any act that violates the rights of another student in academic work, that involves misrepresentation of your own work, or that disrupts the instruction of the course. Other violations include (but are not limited to): cheating on assignments or examinations; plagiarizing, which means copying any part of another’s work and/or using ideas of another and presenting them as one’s own without giving proper credit to the source; selling, purchasing, or exchanging of term papers; falsifying of information; and using your own work from one class to fulfill the assignment for another class without significant modification. Proof of academic misconduct can result in the automatic failure and removal from this course. For questions regarding Academic Integrity, the No-Grade Policy, Sexual Harassment, or the Student Code of Conduct, students are encouraged to refer to the Department Graduate Handbook, the Graduate Catalog, the Ram’s Eye View, and the University website at wcupa.edu.

STUDENTS WITH DISABILITIES

If you have a disability that requires accommodations under the Americans with Disabilities Act (ADA), please present your letter of accommodations and meet with me as soon as possible so that I can support your success in an informed manner. Accommodations cannot be granted retroactively. If you would like to know more about West Chester University’s Services for Students with Disabilities (OSSD), please visit them at 223 Lawrence Center. The OSSD hours of Operation are Monday – Friday, 8:30 a.m. – 4:30 p.m. Their phone number is 610-436-2564, their fax number is 610-436-2600, their email address is ossd@wcupa.edu, and their website is at wcupa.edu/ussss/ossd.

REPORTING INCIDENTS OF SEXUAL VIOLENCE

West Chester University and its faculty are committed to assuring a safe and productive educational environment for all students. In order to meet this commitment and to comply with Title IX of the Education Amendments of 1972 and guidance from the Office for Civil Rights, the University requires faculty members to report incidents of sexual violence shared by students to the University's Title IX Coordinator, Ms. Lynn Klingensmith. The only exceptions to the faculty member's reporting obligation are when incidents of sexual violence are communicated by a student during a classroom discussion, in a writing assignment for a class, or as part of a University-approved research project. Faculty members are obligated to report sexual violence or any other abuse of a student who was, or is, a child (a person under 18 years of age) when the abuse allegedly occurred to the person designated in the University protection of minors policy.  Information regarding the reporting of sexual violence and the resources that are available to victims of sexual violence is set forth at the webpage for the Office of Social Equity at .

EMERGENCY PREPAREDNESS

All students are encouraged to sign up for the University’s free WCU ALERT service, which delivers official WCU emergency text messages directly to your cell phone. For more information, visit wcupa.edu/wcualert. To report an emergency, call the Department of Public Safety at 610-436-3311.

ELECTRONIC MAIL POLICY

It is expected that faculty, staff, and students activate and maintain regular access to University provided e-mail accounts. Official university communications, including those from your instructor, will be sent through your university e-mail account. You are responsible for accessing that mail to be sure to obtain official University communications. Failure to access will not exempt individuals from the responsibilities associated with this course.

TENTATIVE SCHEDULE:

|Wk |Topic |Chapters |

|1 |Introduction of Randomization and Confounding |1 |

| | |CLO1, |

| | |CLO2 |

|2 |Observational Studies |1 |

| | |CLO3, |

| | |CLO4 |

|3 |Propensity Scores: Derivations though Logistic Regression & Covariate |2,3 |

| |adjustment |CLO2, |

| | |CLO3 |

|4 |Propensity Score: Weighting, Matching, Goodness-of-Fit |3,5 |

| | |CLO2, CLO3, |

| | |CLO7 |

|5 |Exam 1 |CLO1, CLO2, CLO3, |

| | |CLO4, |

| | |CLO7 |

|6 |Propensity Score adjustment for more than 2 group designs |5 |

| | |CLO1, CLO2, CLO3, |

| | |CLO6 |

|7 |Introduction to Instrumental Variables |6 |

| | |CLO1, CLO2, |

| | |CLO5 |

|8 |Modeling with Instrumental Variables: PROC SYSLIN, PROC QLIM, PROC REG |6 |

| | |CLO1, |

| | |CLO2, |

| | |CLO5, |

| | |CLO7 |

|9 |Introduction to the Structual Nested Mean Model |10 |

| | |CLO1, |

| | |CLO2, |

| | |CLO5 |

|10 |EXAM 2 |CLO1, |

| | |CLO2, |

| | |CLO3, |

| | |CLO4, |

| | |CLO5, |

| | |CLO6, |

| | |CLO7 |

|11 |Potential Outcomes Framework |Rubin – I |

| | |CLO8 |

|12 |Causal Inference Based on the assignment mechanism |Rubin – II |

| | |CLO1, |

| | |CLO2, |

| | |CLO8 |

|13 |Causal Inference Based on the assignment mechanism (continued) |Rubin - II CLO1, |

| | |CLO2, |

| | |CLO8 |

|14 |Causal inference based on predictive distributions of potential outcomes |Rubin –III |

| | |CLO1, |

| | |CLO2, |

| | |CLO8 |

| |PROJECT |CLO9 |

| |FINAL –TBA |CLO1-CLO9 |

Important Dates:

Last day to drop

Last day to withdraw

First Exam –

Second Exam –

Final Exam -

Project –

Bibliography

1. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics 2002; 58: 21-29. DOI: 10.1111/j.0006-341X.2002.00021.x.

2. Rubin D. Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics 2004; 31: 161-170. DOI: 10.1111/j.1467-9469.2004.02-123.x.

3. Mealli F, Imbens GW, Ferro S, Biggeri A. Analyzing a Randomized Trial on Breast Self-Examination with Noncompliance and Missing Outcomes, Biostatistics 2004; 5: 207-222. DOI: 10.1093/biostatistics/5.2.207.

4. Ten Have T, Joffe M, Lynch K, Maisto S, Brown G, Beck A. Causal mediation analyses with rank preserving models, Biometrics 2007; 63: 926-934. DOI: 10.1111/j.1541-0420.2007.00766.x.

5. Robins J, Rotnitzky A. Estimation of treatment effects in randomized trials with non-compliance and dichotomous outcome using structural mean models. Biometrika 2005; 91: 763-783. DOI: 10.1093/biomet/91.4.763.

6. Imbens GW, Rubin DB. Bayesian inference for causal effects in randomized experiments with noncompliance, Annals of Statistics 1997; 25: 305-327. DOI: 10.1214/aos/1034276631.

7. Jo B, Muthen B. Longitudinal Studies with Intervention and Noncompliance: Estimation of Causal Effects in Growth Curve Mixture Modeling. In Multilevel Modeling: Methodological Advances, Issues, and Applications, eds. N Duan and S.P. Reise, Lawrence Erlbaum: New York, 2001; 51-62.

8. Mealli F, Rubin D. Commentary: Assumptions allowing the estimation of direct causal effects. Journal of Econometrics 2003; 112: 79-87. DOI: 10.1016/S0304-4076(02)00150-1.

9. Ten Have T, Elliot MR, Joffe M., Zanutto E, Datto C. Causal models for randomized physician encouragement trials in treating primary care depression, Journal of the American Statistical Association 2004; 99: 16-25. DOI: 10.1198/016214504000000034.

10. Brown GK, Ten Have T, Henriques GR, Xie SX, Hollander EJ, and Beck AT. Cognitive Therapy for the Prevention of Suicide Attempts: A Randomized Controlled Trial. Journal of the American Medical Association 2005; 294: 2847-2848. DOI: 10.1001/jama.294.22.2848.

11. Rubin DB. Bayesian inference for causal effects – Role of randomization. Annals of Statistics 1978; 6: 34-58. DOI: 10.1214/aos/1176344064.

12. Rubin D. Statistics and Causal Inference: Comment Which Ifs Have Causal Answers, Journal of the American Statistical Association 1986; 81: 961-962. DOI: 10.2307/2289065

13. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. Journal of the American Statistical Association 1996; 91: 444-455. DOI: 10.2307/2291629.

14. Rubin D. Causal inference through potential outcomes and principal stratification. Statistical Science 2007; 21: 299-309. DOI: 10.1214/088342306000000114.

15. Hirano K, Imbens GW, Rubin DB, Zhou XH. Assessing the effects of an influenza vaccine in an encouragement design. Biostatistics 2000; 1: 69-88. DOI: 10.1093/biostatistics/1.1.69.

16. Gelman A, Rubin DB. Inference from Iterative Simulation Using Multiple Sequences. Statistical Science 1992; 7: 457-472. DOI: 10.1214/ss/1177011136.

17. Bellamy SL, Lin JY, Ten Have TR. An introduction to causal modeling in clinical trials. Clinical Trials 2007; 4: 58-73. DOI: 10.1177/1740774506075549.

18. Lynch KG, Cary M, Gallop R, Ten Have TR. Causal Mediation Analyses for Randomized Trials. Health Services and Outcome Methodology 2008; 8: 57-76. DOI: 10.1007/s10742-008-0028-9.

19. Small D, Ten Have T, Joffe M, Cheng J. Random effects models for analysing efficacy of a longitudinal randomized treatment with non-adherence, Statistics in Medicine 2006; 25: 1981-2007. DOI: 10.1002/sim.2313.

20. Rosenbaum, PR. Observational Studies. Vol. 2. Springer; New York: 2001.

21. Basu A, Heckman JJ, Navarro-Lozano S, Urzua S. Use of instrumental variables in the presence of heterogeneity and self-section: an application to treatments of breast cancer patients. Health Economics. 2007; 16:1133–1157.

22. Mullahy, J. (1997). Instrumental-variable estimation of count data models: Applications to models of cigarette smoking behavior. Review of Economics and Statistics, 79, 586–593.

23. Terza, J., Basu, A., Rathouz, P. (2008). Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. Health Economics, 27, 527-543.

24. Gallop, R., Small, D., Lin, J.Y., Elliott, M.R., Joffe, M., and Ten Have, T.R.(2009). Mediation analysis with Principal Stratification, Statistics in Medicine. 28, 1108-1130.

25. Faries D., Leon, A.C., Haro, J.M., and Obenchain, R.L. (2010). Analysis of Observational Healthcare Data Using SAS, SAS Institute Inc: Cary, NC.

26. Rosenbaum (2002), Observational Studies, 2ndEdition, New York:Springer- Verlag.

STA542: Statistical Methods for Observational Studies

Course Description:

In the assessment of the association between a predictor and a response confounding by another factor might yield wrong answers. One standard technique to protect against confounding is randomization, which is the standard for conducting randomized clinical trials (RCT). In the setting where randomization cannot be applied, such as cohort or case-control studies, the potential for confounding exists; therefore, analytical techniques must be developed to address this potential confounding. These studies where the respective predictor is observed (i.e. gender, case versus control, etc…) rather than randomized (i.e. Drug versus placebo, Treatment 1 versus Treatment 2, etc…) are referred to as observational studies. This course will cover statistical methods for the design and analysis of observational studies. Students will be exposed to discussion of differences between experimental, observational, and quasi-experimental studies. Techniques to assess statistical effects while addressing confounding (both measured and unmeasured) and selection bias will be introduced. Various techniques introduced are: propensity scores, inverse probability weighting, instrumental variables, Marginal Structural Models, Structural Nested Mean Models. Students additionally will be introduced to the Rubin Causal Model framework in the assessment of Causal effects.

Text Books:

Faries D., Leon, A.C., Haro, J.M., and Obenchain, R.L. (2010). Analysis of Observational Healthcare Data Using SAS, SAS Institute Inc: Cary, NC.

P. Rosenbaum (2002), Observational Studies, 2ndEdition, New York:Springer- Verlag.

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