Course Files Readings, Examples Stat209 2009

Stat 209-- Course Files, Readings, Examples

Week 1--Course Introduction; properties of regression models
In the news
NY Times endorses R: Data Analysts Captivated by R's Power
1. c.f. case study #6. Surge in teen prenancy (TV news: blame Juno). Teen Birth Rate Up in 26 States in 2006
2. why here is better than New Haven. How the city hurts your brain Marc Berman, John Jonides and Stephan Kaplan "The Cognitive Benefits of Interacting with Nature," Psychological Science.
3. why this class is at 2:15 PM. Skimping on sleep linked to hardened arteries
Short Sleep Duration and Incident Coronary Artery Calcification Christopher Ryan King; Kristen L. Knutson; Paul J. Rathouz; Steve Sidney; Kiang Liu; Diane S. Lauderdale JAMA. 2008;300(24):2859-2866.

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. 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;
MB Ch.6. esp 6.1.4 adjusted variables; 6.6 Interpreting regression coefficients, 6.8 errors in variables
Background piece: Correlation and Causation: A Comment, Stephen Stigler Perspectives in Biology and Medicine, volume 48, number 1 supplement (winter 2005)
Background info. Short primer on test reliability

Additional Resources
Mosteller-Tukey, Chap 13 (Woes of regression coefficients)
Berk, Chap. 6,7 (Using and Interpreting Multiple Regression) Berk online: Chap 6 (esp 6.5,6.6) Chap7
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
Born to Be a Trader? Fingers Point to Yes publication, John M. Coates, Mark Gurnell,and Aldo Rustichini. Second-to-fourth digit ratio predicts success among high-frequency financial traders. PNAS 2009 106:623-628.
Math and Ring finger
Science See Those Fingers? Do the Math   Slashdot Boys with Longer Ring Fingers are Better at Math
Publication . Digit ratio as an indicator of numeracy relative to literacy in 7-year-old British schoolchildren: Brosnan, Mark J. Source: British Journal of Psychology, Volume 99, Number 1, February 2008 , pp. 75-85(11)
Additional data: Univ of Bath academic staff

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

Paul Holland, Encouragement Designs. 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.

A multi-decade example: 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. 415–18.       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"
Overdoing a good thing? Evidence-based medicine.    Hazardous journey Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials Gordon C S Smith, professor1, Jill P Pell, consultant2BMJ 2003;327:1459-1461 (20 December), doi:10.1136/bmj.327.7429.1459
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
By class time, I've consumed 5 big mugs of coffee
1. Three cups of brewed coffee a day 'triples risk of hallucinations'     Caffeine Can Cause Hallucinations   publication (in press) Caffeine, stress, and proneness to psychosis-like experiences: A preliminary investigation. Simon R. Jones and Charles Fernyhougha Personality and Individual Differences
2. But Coffee reduces Alzheimer's risk    Coffee Strong Enough to Ward Off Dementia?

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. (Freedman Ch.4 has technical background on regression)
  more on response schedules (text sec 5.4) in Statistical Models for Causation: A critical review
MB 13.1. Composite scores from multiple indicators.
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

In the news
Videogames
1. No Significant Relationship Between Violent Games, School Shootings    Slashdot   publication: Christopher Ferguson (2009) "The School Shooting/Violent Video Game Link: Causal Relationship or Moral Panic?", Journal of Investigative Psychology and Offender Profiling.
But... CNN: Violent video games linked to child aggression    publication: Longitudinal Effects of Violent Video Games on Aggression in Japan and the United States Craig A. Anderson, Akira Sakamoto, Douglas A. Gentile, Nobuko Ihori, Akiko Shibuya, Shintaro Yukawa, Mayumi Naito, and Kumiko Kobayashi Pediatrics 2008; 122: e1067-e1072.

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. 4027–30. 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
Maindonald-Braun Chap 10, esp 10.2, 10.5, 10.7-9
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 Studies—Biases, 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) 1929–1952

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, 38–43.
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. 1518–22; with discussion, vol. 94 (1999) pp. 352–57.

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. more video games Nintendo brain-trainer 'no better than pencil and paper'      Nintendo’s Brain Age doesn’t work, researcher says
previously, Computer game boosts maths scores
2. An example of interactions [old]. 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)
     HSB ancova handout      data for HSB ancova
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
MB sec 7.3 ("fitting multiple lines")
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, 309–317.
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

In the news
Breast cancer risk drops after women stop hormone use   Breast Cancer's Decline Analyzed Study Credits 2002 Warning on Hormone-Replacement Drugs
NEJM publication: Breast Cancer after Use of Estrogen plus Progestin in Postmenopausal Women, N Engl J Med 2009;360:57387.

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
For lab 3. Two-stage Least Squares in R (tsls in sem package) by John Fox
(Alternatives AER: Applied Econometrics with R    systemfit)
Structural Equation Models Appendix to An R and S-PLUS Companion to Applied Regression John Fox January 2002 Structural Equation Modeling With the sem Package in R John Fox STRUCTURAL EQUATION MODELING,13(3),465–486     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
Rindfus example (Freedman Chap 8; paper reprinted in Freedman text). Education and Fertility: Implications for the Roles Women Occupy Ronald R. Rindfuss; Larry Bumpass; Craig St. John American Sociological Review, Vol. 45, No. 3. (Jun., 1980), pp. 431-447.   from 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:
Course case study (see main page): Does Television Cause Autism? and should instrumental variables (IV) provide the answer?
Also,   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
1. Closure? on, Do vaccines cause autism? U.S. Court Finds No Link Between Vaccines, Autism   youtubevideo
2. 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. 535–544, 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: 691–713.
On regression adjustments to experimental data Advances in Applied Mathematics vol. 40 (2008) pp. 180–93.

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, 21–29.
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
Number Of Fast-food Restaurants In Neighborhood Associated With Stroke Risk     U.S. study ties fast food to stroke risk     More Fast-Food Joints in Neighborhoods Mean More Strokes

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
MB sec 13.2 "Propensity scores in regression"
Non-technical overview      Donald Rubin Nonrandomized Comparative Clinical Studies   another version, Annals of Internal Medicine
Berk, section 11.4.2
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)1223–1231 [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
A broad review of matching and bias-reduction methods. Opiates for the Matches: Matching Methods for Causal Inference Jasjeet S. Sekhon. 2/18/2009
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
More on diets: randomized trial, pre-post data. In 4-diet study, all lost weight if they watched their calories   Best Diet? The One You'll Follow: Study Shows Weight Loss Is Similar in Four Types of Diets
Publication: Comparison of Weight-Loss Diets with Different Compositions of Fat, Protein, and Carbohydrates New England Journal of Medicine Volume 360:859-873, February 26, 2009

Lecture topics
1. Measurement of change: time-1,time-2 data   data example for handout   data analysis
2. Random-effects models for longitudinal data (individual growth curves)
3. Lord's paradox and revisiting regression adjustments for pre-post designs
4. Comparing groups on multiple measurements: repeated measures anova etc
     urea synthesis, BK data     Stat141 analysis     data,,,,,,,,,,,,,,,,,,, example analyses
5. Special topics
  a. Crossover designs
  b. Interrupted Time-series designs Gene Glass overview
  c. Current implementations of value-added analysis

Week 9 Readings
Primary Readings
MB section 10.5, "Repeated measurements in time"; MB Chap 9 "Time series models"
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. 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

2. 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)

3. 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
Multilevel package has behavioral scienes applications including estimates of within-group agreement, and routines using random group resampling (RGR) to detect group effects.

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): 543–559.
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), 613–623.

Dead Week meeting 3/10

Improved! Collection of scanned course handouts, weeks 1-9.
HW 9, solutions. Longitudinal data analysis: repeated measures anova (problem 2) and fitting collection of growth curves (problem 3 demonstration).
Discuss Exam 3 (3/18 7PM in seqouia 200)

Collect TH2 papers 3/12