Stat 209-- Course Files, Readings, Examples
Week 1--Course Introduction; properties of regression models
In the news
Do vaccines cause autism?
Autism Cases Still Going Up As Vaccine Mercury Removed
Continuing Increases in Autism Reported to California's Developmental Services System
Robert Schechter, MD, MSc; Judith K. Grether, PhD Arch Gen Psychiatry. 2008;65(1):19-24.
Background: Vaccines and Autism. The CDC statement a pending court case
Lecture topics
Quick Tour of course and course materials
Main Topic: Meaning of regression coefficients: simple and multiple regression (including logistic)
Technical facts and foibles:
a. adjusted variables and regression coefficients--values of coefficients depend crucially on what else is used in the regression fit
conditioning vs controlling
b. effects of errors in measurement on regression coefficients
Lecture materials
MT woes of regression coefficients slides
Handout 4/4. Coleman data: adjusted-variables multiple regression data file, 20 schools
Week 1 Readings
Primary Readings
Freedman text Ch. 1 (esp. Yule on paupers, Snow on Cholera and Sec 1.5);(Ch 2-4 are advanced review of regression models)
Chap 1 exs also in From Association to Causation: Some Remarks on the History of Statistics;
Berk, Chap. 6,7 (Using and Interpreting Multiple Regression) Berk online: Chap 6 (esp 6.5,6.6) Chap7
Correlation and Causation: A Comment, Stephen Stigler
Perspectives in Biology and Medicine, volume 48, number 1 supplement (winter 2005)
After the fact
Short primer on test reliability
Example of the Use of Regression methods in non-experimental research:
Sex in Media Drives Earlier Teen Passion actual study, Pediatrics, 2006 Sexy Media Matter: Exposure to Sexual Content in Music, Movies, Television, and Magazines Predicts Black and White Adolescents' Sexual Behavior UNC Teen Media Center (NICHD funded)
Additional Resources
Mosteller-Tukey, Chap 13 (Woes of regression coefficients)
Errors of Measurement in Statistics, W. G. Cochran , Technometrics, Vol. 10, No. 4. (Nov., 1968), pp. 637-666.
JStor URL esp sections 8,9,11
Some Effects of Errors of Measurement on Multiple Correlation, W. G. Cochran Journal of the American Statistical Association Vol. 65, No. 329 (Mar., 1970), pp. 22-34
JStor URL esp sec 8 discussion.
Week 2-- Experiments vs observational studies; Neyman-Rubin-Holland formulation
In the news
Effectiveness of Cholesterol Drugs. Cholesterol Drug Bombs, New York Times editoria
l
Lecture topics
A. Continue overview regression models--standardized coefficients; dichotomous outcomes, (logit and probit models)
B. Spurious Correlation: some historical notes; partial and part correlations, mediating variables ( link2 link3 culled from quick Google)
C. First pass: experiments vs observational studies
Surveys of results from experimental and observational studies (see HRT below)
D. Introduction to Neyman-Rubin-Holland formulation for causal effects.
presentation of NRH formulation for comparative studies based on Appendix of Holland (1988)
Illustration using encouragement design representation in Holland (1988). copies of selected overheads.
Primary Readings
Freedman text Ch. 1 (esp Snow on Cholera and Sec 1.5); dichotomous outcomes, Ch 6, secs 6.2-6.4 (incl Catholic schools study); value of modeling Chap 8 secs 8.10-8.12; response schedules sec 5.4
Freedman Chap 1 exs also in From Association to Causation: Some Remarks on the History of Statistics;
and Statistical Models and Shoe Leather, Sociological Methodology, Vol. 21. (1991), pp. 291-313. JStor link
Causal Inference, Path Analysis, and Recursive Structural Equations Models Paul W. Holland Sociological Methodology, Vol. 18. (1988), pp. 449-484.
Abstract Rubin's model for causal inference in experiments and observational studies is enlarged to analyze the problem of "causes causing causes" and is compared to
path analysis and recursive structural equations models. A special quasi-experimental design, the encouragement design, is used to give concreteness to the discussion by
focusing on the simplest problem that involves both direct and indirect causation. It is shown that Rubin's model extends easily to this situation and specifies conditions
under which the parameters of path analysis and recursive structural equations models have causal interpretations.
After the fact Experiments vs Observational studies, Hormone Replacement Therapy
D.B. Petitti and D.A. Freedman. Invited commentary: How far can epidemiologists get with statistical adjustment? American Journal of Epidemiology vol. 162 (2005) pp. 41518. Freedman handout page
Additional Resources
Spurious correlation?
Correlations Genuine and Spurious in Pearson and Yule, John Aldrich Statistical Science, Vol. 10, No. 4. (Nov., 1995), pp. 364-376. Jstor link
Spurious Correlation: A Causal Interpretation. Herbert A. Simon Journal of the American Statistical Association, Vol. 49, No. 267. (Sep., 1954), pp. 467-479. Jstor link
Experiments vs Observational studies:
Mosteller-Tukey Ch. 13 (esp sec 13G)
Bringing Evidence-Driven Progress To Education:
main
report November 2002 US
DOE press release December 2003 confab, "what works"
Classic paper on Medical experimentation.
Statistics and Ethics in Surgery and Anesthesia. John P. Gilbert; Bucknam McPeek; Frederick Mosteller Science, New Series, Vol. 198, No. 4318. (Nov. 18, 1977), pp. 684-689.
JTSOR link
Neyman-Rubin-Holland models for comparative experiments (causal inference)
Berk, Chap 5, 10.5 pdf of chap5
Rosenbaum Ch 2 (esp 2.5)
Statistics and Causal Inference, Paul W. Holland pp. 945-960 JASA 1986, another JSTOR link
Commentaries Donald Rubin, David Cox
Rubin, D. B., 1974, Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies, Journal of Educational Psychology, 66, 688-701.
Winship's repository Counterfactual
Causal Analysis in Sociology
Useful intro lecture notes from Jonathan Wand, Political Science
Counterfactual wiki page Winship repository long Nancy Cartwright
Week 3-- Path analysis and causal modeling multiple regression with pictures
In the news
Lecture topics
1. Path analysis introduction and examples (incl Blau-Duncan from Freedman chap 5). class handout
2. Structural equation models: introduction and examples. class handout
3. Does path analysis identify causal effects? Demonstrations of failure for Holland's encouragement design, Rogosa longitudinal examples.
class handout Encouragement design slides
Week 3 Readings
Primary Readings
Freedman text Chap 5.
more on response schedules (text sec 5.4) in Statistical Models for Causation: A critical review
Paul Holland: Encouragement design results; sections 3-5 Causal Inference, Path Analysis, and Recursive Structural Equations Models Paul W. Holland Sociological Methodology, Vol. 18. (1988), pp. 449-484.
David Rogosa. Casual Models Do Not Support Scientific Conclusions: A Comment in Support of Freedman.
Journal of Educational Statistics, Vol. 12, No. 2. (Summer, 1987), pp. 185-195.
Jstor link
Theme Song Ballad of the casual modeler http://www.stanford.edu/class/ed260/ballad.mp3
Additional Resources
Technical details on Rogosa longitudinal examples:
Rogosa, D. R. (1993). Individual unit models versus structural equations: Growth curve examples.
In Statistical modeling and latent variables, K. Haagen, D. Bartholomew, and M. Diestler, Eds. Amsterdam: Elsevier North Holland, 259-281.
Rogosa, D. R., & Willett, J. B. (1985). Satisfying a simplex structure is simpler than it should be.
Journal of Educational Statistics, 10, 99-107. Jstor link
original publication on the longitudinal path analysis: Some Models for Analysing Longitudinal Data on Educational Attainment. Harvey Goldstein
Journal of the Royal Statistical Society. Series A (General), Vol. 142, No. 4. (1979), pp. 407-442. Jstor link
Path analysis intros
Path Analysis: Sociological Examples. Otis Dudley Duncan The American Journal of Sociology, Vol. 72, No. 1. (Jul., 1966), pp. 1-16. Jstor link
D.A. Freedman, Comments on Standardizing Path Diagrams: What Are the Parameters?
A recent reconsideration by a wise psychologist: The Path Analysis Controversy: A new statistical approach to strong appraisal of verisimilitude
Meehl, Paul E; Waller, Niels G Psychological Methods. Vol 7(3), Sep 2002, pp. 283-300. available from SU PsychInfo database
Structural equation modeling is a major industry in social and behavioral science with many texts (such as Principles and Practice of Structural Equation Modeling 2nd Edition Rex B. Kline; here's a long list), specialized courses (U. C Irvine MGMT 290 NC state PA 765), dedicated journals (Structural Equation Modeling: A Multidisciplinary Journal), and specialized computer programs (e.g., LISREL, EQS, AMOS).
Maximum likelihood factor analysis: A General Method for Analysis of Covariance Structures, K. G. Joreskog, Biometrika, Vol. 57, No. 2. (Aug., 1970), pp. 239-251.
Structural equation modeling from Scientific Software International
home of * Structural Equation Modeling (LISREL) Student
editions, documentation, examples, etc
sem Structural Equation Models package in R, sem manual
Two good structural equation model reviews:
Structural Equation Models William T. Bielby; Robert M. Hauser Annual Review of Sociology, Vol. 3. (1977), pp. 137-161. JStor link
Breckler, S. J. (1990). Applications of Covariance Structure Modeling in Psychology: Cause for Concern? Psychological Bulletin, 107, 260-273. here's a link that may be permanent
Week 4-- Multilevel data: Contextual effects, aggregation bias, random-effects (mixed) models
Lecture topics
1. Background: nested data, ecological fallacy, aggregation bias, levels of analysis.
2. Traditional approaches to multilevel analysis: contextual effects, school effects.
3. Advanced multilevel analyses: random effects models, linear and non-linear.
Week 4 Readings
Primary Readings
Aggregation bias, Ecological fallacy.
D.A. Freedman. "Ecological inference and the ecological fallacy." International Encyclopedia for the Social and Behavioral Sciences. Elsevier (2001) vol. 6 pp. 402730. N. J. Smelser and Paul B. Baltes, eds. A one-page version: D.A. Freedman. "The ecological fallacy." In the Encyclopedia of Social Science Research Methods. Sage Publications (2004) Vol. 1 p. 293. M. Lewis-Beck, A. Bryman, and T. F. Liao, eds
A good sociological/medical overview. Ecological effects in multi-level studies. Blakely TA, Woodward AJ. J Epidemiol Community Health. 2000 May;54(5):367-74. full text
Current statistical analyses in social science: multilevel models. Also Lab2, 4/30/07
Berk 10.3
History of multilevel models from Scientific Software International, Inc
Using SAS PROC MIXED:
Judith Singer HLM/PROC Mixed papers: Multilevel
Modelling Newsletter ; or
JEBS1998 Using SAS PROC MIXED to Fit Multilevel Models, Jstor
Using R, lme, nlme.
John Fox lme tutorial Fitting linear mixed models in R Using the lme4 package Douglas Bates (pp.27-30)
Additional Resources
Aggregation bias, Ecological fallacy.
D.A. Freedman. "The ecological fallacy." In the Encyclopedia of Social Science Research Methods. Sage Publications (2004) Vol. 1 p. 293. M. Lewis-Beck, A. Bryman, and T. F. Liao, eds
A Rule for Inferring Individual-Level Relationships from Aggregate Data, Glenn Firebaugh American Sociological Review Vol. 43, No. 4 (Aug., 1978), pp. 557-572 JStor URL
A good sociological/medical overview. Ecological effects in multi-level studies. Blakely TA, Woodward AJ. J Epidemiol Community Health. 2000 May;54(5):367-74. pubmed full text
American Journal of Epidemiology Vol. 139, No. 8: 747-760 Invited Commentary: Ecologic StudiesBiases, Misconceptions, and Counterexamples S Greenland, J Robins
The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology J. Michael Oakes Social Science & Medicine 58 (2004) 19291952
Educational multilevel data.
The Analysis of Multilevel Data in Educational Research and Evaluation Leigh Burstein Review of Research in Education, Vol. 8. (1980), pp. 158-233. Jstor link
Methodological Advances in Analyzing the Effects of Schools and Classrooms on Student Learning, Stephen W. Raudenbush; Anthony S. Bryk Review of Research in Education, Vol. 15. (1988 - 1989), pp. 423-475. Jstor link
Analyzing Multilevel Data in the Presence of Heterogeneous within-Class Regressions Leigh Burstein; Robert L. Linn; Frank J. Capell
Journal of Educational Statistics, Vol. 3, No. 4. (Winter, 1978), pp. 347-383. Jstor link
examples from analyses of voting data.
Bias in ecological regression Stephen Ansolabehere and Douglas Rivers
David A. Freedman et al., "Ecological Regression and Voting Rights," Evaluation Review 1991, pp. 673-711, Berkeley Law postimg
Klein, S. P. and Freedman, D. A. (1993), "Ecological regression in voting rights cases," Chance, 6, 3843.
D.A. Freedman, S.P. Klein, M. Ostland, and M.R. Roberts. "Review of 'A Solution to the Ecological Inference Problem.' " Journal of the American Statistical Association, vol. 93 (1998) pp. 151822; with discussion, vol. 94 (1999) pp. 35257.
Current statistical analyses in social science: multilevel models.
Using SAS PROC mixed:
Fitting Nonlinear Mixed Models with the New NLMIXED Procedure, Russell D. Wolfinger, SAS Institute Inc., Cary, NC
Judith Singer HLM/PROC Mixed papers: Multilevel
Modelling Newsletter ; JEBS1998 Using SAS PROC MIXED to Fit Multilevel Models, Jstor
HLM - Hierarchical Linear and Nonlinear Modeling (HLM): descriptions and student edition HLM6
Freedman, D. A. (census adjustments). Hierarchical Linear Regression
Using R: lme4 (lmer and nlme) and mlmRev. John Fox lme tutorial Doug Bates SASmixed package U. Washington, Hierarchical Modeling for the Social Sciences
Fitting linear mixed models in R Using the lme4 package Douglas Bates (pp.27-30)
London exam data example in Examples from Multilevel Software Comparative Reviews
Douglas Bates
mlmRev data examples. Also, Tennessee's Student Teacher Achievement Ratio (STAR) from Creating an R data set from STAR Douglas Bates
STATA does it also
HLM6 student edition HLM setup for HSB example
Week 5.--The many uses and forms of analysis of covariance (including regression discontinuity designs)
In the news
1. Physically Active People Age More Slowly also SJ Merc 1/29/08. Exercisers' DNA looks less frazzled
Actual publication : The Association Between Physical Activity in Leisure Time and Leukocyte Telomere Length. Arch Intern Med. 2008;168(2):154-158.
2. An example of interactions. Aspirin may be less effective heart treatment for women than men
Aspirin Resistance in Patients with Stable Coronary Artery Disease, in the Annals of Pharmacotherapy April 2007
Lecture topics
1. Review: formulation and purposes of analysis of covariance (including role in multilevel analysis)
2. Analyzing treatment effects as a function of covariate(s) such as Johnson-Neyman technique
3. Uses of ancova with haphazard and with systematic assignment. Failures of ancova regression adjustments in observational studies. Non-random assignment on the basis of the covariate, such as regression discontinuity designs.
Week 5 Readings
Primary Readings
Freedman section 5.6
Berk section 8.3 pdf Berk chap 8
Rogosa, D. R. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88, 307-321. [an alternative scan from the APA site]
Regression Discontinuity Designs Useful primers by Wm Trochin: The regression-discontinuity design regression-discontinuity analysis
Rubin, D. B., (1977), "Assignment to a Treatment Group on the Basis of a Covariate", Journal of Educational Statistics, 2, 1-26. Jstor link
Additional Resources
analysis of covariance: Background/historical papers:
Weisberg, H. I. Statistical adjustments and uncontrolled studies. Psychological Bulletin, 1979, 86, 1149-1164.
Covariance Adjustment in Randomized Experiments and Observational Studies Paul R. Rosenbaum Statistical Science, Vol. 17, No. 3. (Aug., 2002), pp. 286-304. Jstor
Some Aspects of Analysis of Covariance, A Biometrics Invited Paper with Discussion. D. R. Cox; P. McCullagh Biometrics, Vol. 38, No. 3, (Sep., 1982), pp. 541-561. Jstor
Analysis of Covariance: Its Nature and Uses William G. Cochran Biometrics, Vol. 13, No. 3, Special Issue on the Analysis of Covariance. (Sep., 1957), pp. 261-281. Jstor
The Use of Covariance in Observational Studies W. G. Cochran Applied Statistics, Vol. 18, No. 3. (1969), pp. 270-275. Jstor
Estimation of the Slope and Analysis of Covariance when the Concomitant Variable is Measured with Error James S. Degracie; Wayne A. Fuller Journal of the American Statistical Association, Vol. 67, No. 340. (Dec., 1972), pp. 930-937. Jstor
Deep background Neter-Wasserman text (Applied linear statistical models. Neter, Kutner, Nachtsheim & Wasserman 1996. Fifth edition. Homewood IL: Irwin, Inc.) chapters 22 and 8.
Johnson-Neyman technique and aptitude-treatment interaction (ATI)
Regions of Significant Criterion Differences in Aptitude-Treatment-Interaction Research Leonard S. Cahen; Robert L. Linn American Educational Research Journal, Vol. 8, No. 3. (May, 1971), pp. 521-530. Jstor
Identifying Regions of Significance in Aptitude-by-Treatment-Interaction Research Ronald C. Serlin; Joel R. Levin American Educational Research Journal, Vol. 17, No. 3. (Autumn, 1980), pp. 389-399. Jstor
Defining Johnson-Neyman Regions of Significance in the Three-Covariate ANCOVA Using Mathematica Steve Hunka; Jacqueline Leighton Journal of Educational and Behavioral Statistics, Vol. 22, No. 4. (Winter, 1997), pp. 361-387. Jstor
discussion of substantive issues: Trait-Treatment Interaction and Learning David C. Berliner; Leonard S. Cahen Review of Research in Education, Vol. 1. (1973), pp. 58-94. Jstor
Regression Discontinuity Designs
Short bibliography
Trochim W.M. & Cappelleri J.C. (1992). "Cutoff assignment strategies for enhancing randomized clinical trials." Controlled Clinical Trials, 13, 190-212. pubmed link
Capitalizing on Nonrandom Assignment to Treatments: A Regression-Discontinuity Evaluation of a Crime-Control Program Richard A. Berk; David Rauma Journal of the American Statistical Association, Vol. 78, No. 381. (Mar., 1983), pp. 21-27. Jstor
Berk, R.A. & de Leeuw, J. (1999). "An evaluation of California's inmate classification system using a generalized regression discontinuity design." Journal of the American Statistical Association, 94(448), 1045-1052. Jstor
lecture notes Wisconsin econometrics London School of Economics U Arizona
Econometric treatments using Neyman-Rubin causal formulation.
Another look at the Regression Discontinuity Design
Eligible Non-Participant And Ineligible Individuals As A Double Control Group In Regression Discontinuity Designs, Erich Battistin, Enrico Rettore, Proceedings of Statistics Canada Symposium 2002
more econometrics
the original paper: Thistlewaite, D., and D. Campbell (1960): "Regression-Discontinuity Analysis: An Alternative
to the Ex Post Facto Experiment," Journal of Educational Psychology, 51, 309317.
educational application: regression discontinuity design to examine the causal effect of summer school and grade retention on student achievement.
Week 6.-- Instrumental variable methods, simultaneous equations
Lecture topics
1. Intro IV (Disattenuation, ancova adjustments, "selection effects") and other IV applications for broken regression models
2. Simultaneous equations (2SLS, IV in butter, ed and fertility, Freedman), nonrecursive models
3. Reciprocal effects and non-recursive models in longitudinal data. Empirical research on reciprocal effects (e.g. TV and ADHD), including cross-lagged correlation.
Week 6 Readings
Primary Readings
Freedman, text Chap 8
Berk section 9.5
Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments. Joshua D. Angrist; Alan B. Krueger, The Journal of Economic Perspectives Vol. 15, No. 4 (Autumn, 2001), pp. 69-85
Technical reference. Joshua D. Angrist; Guido W. Imbens; Donald B. Rubin "Identification of Causal Effects Using Instrumental Variables"
Journal of the American Statistical Association, Vol. 91, No. 434. (Jun., 1996), pp. 444-455. JStor note: compliance discussion for week 7
Additional resources
Course case study (see main page): Does Television Cause Autism? and should instrumental variables (IV) provide the answer?
For lab 3. Two-stage Least Squares in R (tsls in sem package) by John Fox (also "systemfit")
Structural Equation Modeling With the sem Package in R John Fox STRUCTURAL EQUATION MODELING,13(3),465486 Jox Fox home page
Fox, J. (1979) Simultaneous equation models and two-stage least-squares. In Schuessler, K. F. (ed.) Sociological Methodology 1979, Jossey-Bass. Jstor
Instrumental variables, Epidemiology exposition: An introduction to instrumental variables for epidemiologists, Sander Greenland, International Journal of Epidemiology 2000;29:722-729 note: compliance discussion for week 7
Application of instrumental variables: The Effect of File Sharing on Record Sales An Empirical Analysis
Effect of job training programs
Reciprocal effects: Rogosa, D. R. (1980). A critique of cross-lagged correlation. Psychological Bulletin, 88, 245-258.
Granger Causality. Nobel 2003. Complete Granger
Relationships--and the Lack Thereof--Between Economic Time Series, with Special Reference to Money and Interest Rates. David A. Pierce Journal of the American Statistical Association, Vol. 72, No. 357. (Mar., 1977), pp. 11-26. Jstor
Week 7.-- Compliance and experimental protocols; encouragement designs; intent to treat
In the news
Compliance case study. New study of Atkins diet shows no adverse effects on women
Comparison of the Atkins, Zone, Ornish, and LEARN Diets for Change in Weight and Related Risk Factors Among Overweight Premenopausal Women: The A TO Z Weight Loss Study: A Randomized Trial
Christopher D. Gardner; Alexandre Kiazand; Sofiya Alhassan; Soowon Kim; Randall S. Stafford; Raymond R. Balise; Helena C. Kraemer; Abby C. King
JAMA. 2007;297:969-977.
Lecture topics
1. Compliance background: Intent-to-treat analyses,
2. Compliance and Dose-response data analysis (Efron-Feldman)
3. IV estimators (Greenland, Angrist et al, week6).
4. Rubin-Holland approach via Booil Jo presentation: Potential Outcomes Approach: A Brief Introduction
Week 7 Readings
Primary Readings
Berk, section 11.4.1
Compliance Background: Intent-to-Treat (ITT), the FDA mandate
simple definitions: wiki FDA, slides 10-16
Intent-To-Treat Analysis Versus As-Treated Analysis Jonas H. Ellenberg, Phd Drug Information Journal, Vol. 30, pp. 535544, 1996
Epidemiology exposition: An introduction to instrumental variables for epidemiologists, Sander Greenland, International Journal of Epidemiology 2000;29:722-729
Additional resources
David Freedman on Compliance Adjustments:
Statistical Models for Causation: What Inferential Leverage Do They Provide? Evaluation Review 2006; 30: 691713.
On regression adjustments to experimental data Advances in Applied Mathematics vol. 40 (2008) pp. 18093.
Intent-to-treat Analysis of Randomized Clinical Trials Michael P. LaValleyBoston University ACR/ARHP Annual Scientific Meeting Orlando 10/27/2003
Compliance as an Explanatory Variable in Clinical Trials. B. Efron; D. Feldman Journal of the American Statistical Association, Vol. 86, No. 413. (Mar., 1991), pp. 9-17. Jstor
What is meant by intention to treat analysis? Survey of published randomised controlled trials Sally Hollis and Fiona Campbell British Medical Journal 1999;319;670-674
Booil Jo, Dept of Psychiatry Estimation of Intervention Effects with Noncompliance Journal of Educational and Behavioral Statistics
Compliance Publications based on Neyman-Rubin causal models:
Direct and Indirect Causal Effects via Potential Outcomes Donald B. Rubin Scandinavian Journal of Statistics Volume 31, Issue 2, Page 161-170, Jun 2004 .
Principal Stratification in Causal Inference Constantine E. Frangakis and Donald B. Rubin, Biometrics, 2002, 58, 2129.
Addressing Complications of Intention-to-Treat Analysis in the Combined Presence of All-or-None Treatment-Noncompliance and Subsequent Missing Outcomes. Constantine E. Frangakis; Donald B. Rubin Biometrika, Vol. 86, No. 2. (Jun., 1999), pp. 365-379. Jstor link
Principal Stratification Approach to Broken Randomized Experiments: A Case Study
of School Choice Vouchers in New York City Barnard, Frangakis, Hill, and Rubin Journal of the American Statistical Association June 2003, Vol. 98, No. 462, Applications and Case Studies
Battistin, E. and Rettore, E. (2002). "Testing for Programme Effects in a Regression Discontinuity Design with Imperfect Compliance." Journal of the Royal Statistical Society A, 165(1), 39-57.
Week 8.-- Matching and propensity score methods
In the news
I nitial Severity and Antidepressant Benefits: A Meta-Analysis of Data Submitted
to the Food and Drug Administration. PLoS Medicine February 2008 | Volume 5 | Issue 2 |
Lecture topics
1. Traditional matching methods: pair matching, Mahalanobis distance. Matching for increased precision or bias-reduction. Case-control studies. Modern Implementations of matching methods (also Lab 4). Ben Hansen matching exs using MatchIt/optmatch
2. The advent/onslaught of propensity score matching methodology for treatment-control comparisons
Week 8 Readings
Primary Readings
Berk, section 11.4.2
Non-technical overview Donald Rubin Nonrandomized Comparative Clinical Studies another version, Annals of Internal Medicine
Joffe, Marshall M. and Paul R. Rosenbaum. 1999. "Invited Commentary: Propensity Scores." American Journal of Epidemiology 150(4):327-33.
Methods to assess intended effects of drug treatment in observational studies are reviewed Journal of Clinical Epidemiology 57(2004)12231231 [an overview of many of past weeks topics]
Rosenbaum and Rubin, Reducing Bias in Observational Studies Using Subclassification on the Propensity Score, JASA 79[387], September 1984, 516-524. JStor [one of the original technical papers]
Additional resources
Strategies for Using Propensity Scores Well. A Workshop given by
Thomas E. Love, Ph. D., Case Western Reserve University 6th International Conference for Health Policy Research October 28, 2005 another version of Love workshop ASA
Introduction to Propensity Score Matching: A New Device for Program Evaluation UNC, Chapel Hill
useful bibliography
R packages and examples:
1. Ben Hansen (local hero) Optimal matching optmatch manual vignettes Hansen presentation: Flexible, Optimal Matching for Comparative Studies Using the optmatch package
Optmatch application paper: Full matching in an observational study of coaching for the SAT.(Scholastic Assessment Test) Journal of the American Statistical Association; 9/1/2004; Hansen, Ben B.
2. MatchIt: Nonparametric Preprocessing for Parametric Casual Inference Daniel Ho, Kosuke Imai, Gary King, Elizabeth Stuart MatchIt provides a wrapper that can call optmatch or Sekhon's genetic matching] Users Guide
Another application (including matchit): Attributing Effects to a Get-Out-The-Vote Campaign Using Full
Matching and Randomization Inference Jake Bowers and Ben Hansen
Also:
3. Multivariate and Propensity Score Matching Software for Causal Inference Jasjeet S. Sekhon
Propensity etc Original Technical Publications [jstor links]
Rosenbaum, P. R. And D. B. Rubin, 1983, The Central Role of the Propensity Score in Observational Studies for Causal Effects, Biometrika 70[1], April 1983, 41-55. JStor
P. Rosenbaum, Chapters 2 and 3 (on exact inference for treatment effects) in Observational Studies, New York: Springer, 1995.
Dropping out of High School in the United States: An Observational Study Paul R. Rosenbaum Journal of Educational Statistics, Vol. 11, No. 3. (Autumn, 1986), pp. 207-224. Jstor
Paul R. Rosenbaum; Donald B. Rubin. "Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score" The American Statistician, Vol. 39, No. 1. (Feb., 1985), pp. 33-38 JStor
D. Rubin, Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies, Statistical Science 5[4], November 1990, 472-480. JStor
Rubin, D. B., 1974, Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies, Journal of Educational Psychology, 66, 688-701.
Rubin, D. B., 1978, Bayesian Inference for Causal Effects: The Role of Randomization,” Annals of Statistics 6[1], January 1978, 34-58. JStor
Week 9. Longitudinal (esp time-1, time-2) data analysis for experimental and non-experimental designs.
Lord's paradox, Measurement of change, Growth Curves, Repeated Measures Analysis of Variance, and even value-added analysis
In the news
1. Skip Breakfast, Pack on the Pounds. Publication Breakfast Eating and Weight Change in a 5-Year Prospective Analysis of Adolescents: Project EAT (Eating Among Teens)
Maureen T. Timlin, Mark A. Pereira, Mary Story, and Dianne Neumark-Sztainer
Pediatrics 2008; 121: e638-e645.
2. More on value of experiments (week 2), HRT worse than we thought. Cancer Risk Stays After Hormone Therapy (Washington Post, with video).
Publication: Health Risks and Benefits 3 Years After Stopping Randomized Treatment With Estrogen and Progestin
Gerardo Heiss; Robert Wallace; Garnet L. Anderson; Aaron Aragaki; Shirley A. A. Beresford; Robert Brzyski; Rowan T. Chlebowski; Margery Gass; Andrea LaCroix; JoAnn E. Manson; Ross L. Prentice; Jacques Rossouw; Marcia L. Stefanick; for the WHI Investigators
JAMA. 2008;299(9):1036-1045.
Lecture topics
1. Lord's paradox and revisiting regression adjustments for pre-post designs
2. Comparing groups on multiple measurements: repeated measures anova etc
urea synthesis, BK data Stat141 analysis
data,,,,,,,,,,,,,,,,,,, example analyses
3. Random-effects models for longitudinal data (individual growth curves)
4. Crossover designs
5. Interrupted Time-series designs Gene Glass overview
6. Current implementations of value-added analysis
Week 9 Readings
Primary Readings
Wainer and Brown Three Statistical Paradoxes in the Interpretation of Group Differences: Illustrated with Medical School Admission and Licensing Data
Comparative Analyses of Pretest-Posttest Research Designs,
Donna R. Brogan; Michael H. Kutner, The American Statistician, Vol. 34, No. 4. (Nov., 1980), pp. 229-232. JSTOR link
Don Rubin on value-added and Lord's paradox: A Potential Outcomes View of Value-Added Assessment in Education
Donald B. Rubin, Elizabeth A. Stuart, and Elaine L. Zanutto, Journal of Educational and Behavioral Statistics
Additional resources
1. Lord's Paradox, pre-post group comparisons.
Lord, F. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68, 304-305.L
Wainer, H. (1991). Adjusting for differential base rates: Lord's Paradox again. Psychological Bulletin, 109, 147-151.
or Wainer and Brown Three Statistical Paradoxes in the Interpretation of Group Differences: Illustrated with Medical School Admission and Licensing Data
a quick low-level read: Lord's Paradox and the Assessment of Change During College Journal of College Student Development, May/Jun 2004 by Pike, Gary R
Another time1-time2 reading covering old-fashioned ground including Lord's paradox. Maris, Eric. (1998). Covariance Adjustment Versus Gain Scores--Revisited. Psychological Methods, 3(3) 309-327. apa link (from campus IP)
2. Repeated measures analysis of variance
Models for Pretest-Posttest Data: Repeated Measures ANOVA Revisited Earl Jennings Journal of Educational Statistics, Vol. 13, No. 3. (Autumn, 1988), pp. 273-280. Jstor
A good R-primer on repeated measures (a lots else). Notes on the use of R for psychology experiments and questionnaires Jonathan Baron, Yuelin Li. Another version
3. Measurement of Change, Growth Curve Analysis.
Longitudinal Data Analysis Examples with Random Coefficient Models. David Rogosa; Hilary Saner . Journal of Educational and Behavioral Statistics, Vol. 20, No. 2, Special Issue: Hierarchical Linear Models: Problems and Prospects. (Summer, 1995), pp. 149-170. Jstor
Demonstrating the Reliability of the Difference Score in the Measurement of Change. David R. Rogosa; John B. Willett Journal of Educational Measurement, Vol. 20, No. 4. (Winter, 1983), pp. 335-343. Jstor
A growth curve approach to the measurement of change.
Rogosa, David; Brandt, David; Zimowski, Michele
Psychological Bulletin. 1982 Nov Vol 92(3) 726-748
4. Value-added analysis.
National School Boards Association: The Value of Value-Added Analysis
J.R. Lockwood, Harold Doran, and Daniel F. McCaffrey. Using R for estimating longitudinal student achievement models. R News, 3(3):17-23, December 2003.
Fitting Value-Added Models in R Harold C. Doran & J.R. Lockwood
Using a Longitudinal Student Tracking System to Improve the Design for Public School Accountability in California Edward H. Haertel, August 2005
5. Interrupted time-series
Interrupted Time Series Quasi-Experiments Gene V Glass Arizona State University
Interrupted time-series analysis and its application to behavioral data
Donald P. Hartmann, John M. Gottman, Richard R. Jones, William Gardner, Alan E. Kazdin, and Russell S. Vaught J Appl Behav Anal. 1980 Winter; 13(4): 543559.
Segmented regression analysis of interrupted time series studies in medication use research. By: Wagner, A. K.; Soumerai, S. B.; Zhang, F.; Ross-Degnan, D.. Journal of Clinical Pharmacy & Therapeutics, Aug2002, Vol. 27 Issue 4, p299-309,
Interrupted Time Series Designs In Health Technology Assessment: Lessons From Two
Systematic Reviews Of Behavior Change Strategies Craig R. Ramsay University Of Aberdeen, International Journal Of Technology Assessment In Health Care, 19:4 (2003), 613623.
Dead Week meeting 3/13
1. Collect TH2 papers
and jury duty status permitting
2. Discuss Exam 3 (3/20 3:30 in seqouia 200)
3. HW 9, solutions.
My intention last week of gliding into the end of quarter came up short, to say the least. Reviewing HW9 will allow completion of repeated measures anova (problem 2) and fitting collection of growth curves (problem 3 demonstration).
New! Collection of scanned course handouts, weeks 1-9.