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1 Course Syllabus PubH 8811: Seminar: Health Services Research Methods Fall 2014 Credits: 3 Meeting Days: Monday and Wednesday, September 3 December 15, 2012 Meeting Time: 10:10 am 11:40 pm Meeting Place: 325 Lind Hall Instructors: Bryan Dowd Office Address: 15-205 PWB Office Phone: 612-624-5468 Fax: 612-624-2196 E-mail: [email protected] Office Hours: By appointment Required text: Greene, William. Econometric Analysis (7 th edition). Prentice Hall: Edgewood Cliffs, New Jersey (2011). Feel free to use earlier editions. Highly recommended text: Kennedy, Peter. Guide to Econometrics (6th edition). Wiley Blackwell (2008). Feel free to use the 5 th edition (MIT Press: Cambridge, MA.,1993). Other good texts: Baum, Christopher F. An Introduction to Modern Econometrics Using Stata. Stata Press. (2006). Errata at http://www.stata-press.com/books/errata/imeus.html. Cameron, A. Colin and Pravin K. Trivedi. Microeconometrics: Methods and Applications: 2 nd Edition. Cambridge University Press (2010). Cameron, A. Colin and Pravin K. Trivedi. Microeconometrics Using Stata, Revised Edition / Edition 2. StataCorp LP. (2008). Wooldridge, Jeffrey M. Econometric Analysis of Cross-Section and Panel Data. MIT Press (2002).

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Course Syllabus

PubH 8811: Seminar: Health Services Research Methods

Fall 2014

Credits: 3

Meeting Days: Monday and Wednesday, September 3 – December 15, 2012

Meeting Time: 10:10 am – 11:40 pm

Meeting Place: 325 Lind Hall

Instructors: Bryan Dowd

Office Address: 15-205 PWB

Office Phone: 612-624-5468

Fax: 612-624-2196

E-mail: [email protected]

Office Hours: By appointment

Required text:

Greene, William. Econometric Analysis (7th

edition). Prentice Hall: Edgewood Cliffs, New Jersey

(2011). Feel free to use earlier editions.

Highly recommended text:

Kennedy, Peter. Guide to Econometrics (6th edition). Wiley Blackwell (2008). Feel free to use the

5th

edition (MIT Press: Cambridge, MA.,1993).

Other good texts:

Baum, Christopher F. An Introduction to Modern Econometrics Using Stata. Stata

Press. (2006). Errata at http://www.stata-press.com/books/errata/imeus.html.

Cameron, A. Colin and Pravin K. Trivedi. Microeconometrics: Methods and Applications: 2nd

Edition. Cambridge University Press (2010).

Cameron, A. Colin and Pravin K. Trivedi. Microeconometrics Using Stata, Revised Edition /

Edition 2. StataCorp LP. (2008).

Wooldridge, Jeffrey M. Econometric Analysis of Cross-Section and Panel Data. MIT Press (2002).

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Description and Course Objective:

The course covers problems encountered in empirical health services research. The course is

organized around different estimation problems which arise in health services research data and

covers, for each problem:

a. A description of the problem

b. The consequences of the problem,

c. The methods used to deal with the problem, and

d. A review of the health services research studies which have applied (or

should have applied) those methods

In class we cover (a) through (c), with references to (d). The applied readings primarily are left as

out-of-class assignments, so that we can cover more topics in class.

An important part of the course is the computer exercises that allow the student to analyze data from

health services research projects using the methods covered in class. I will distribute a handout for

reading our MEPS dataset into R, but our exercises will assume you’re using (any version of ) Stata.

Upon completion of the course, the student should be able to take descriptions of applied health

services research problems, model the problem, identify potential problems in estimating the

parameters of interest, and apply appropriate estimation procedures.

Grading:

Grading is based on three exams (which are weighted equally) and a set of shorter assignments

including computer exercises. To receive an A in the course, students must have an A average on the

exams, and turn in all the computer assignments. Shorter assignments typically are due one week

after they are assigned. Late assignments are marked down one letter grade. Shorter assignments

may not be turned in after the last class meeting. All computer assignments must be completed to

receive a passing grade in the course. Students may collaborate on shorter assignments, but

collaborating on exams is strictly forbidden and can result in dismissal from the program.

Assumed Level of Knowledge:

It is assumed that the student has completed, minimally, Biostatistics 7401-7402 with a B average.

Thus, the following sections of Greene's text are assumed to be familiar to the student: Chapters 1-4

and Appendices A-D.

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Schedule of Topics and Readings:

Class

Meeting Topic

1,2 Causal modeling and properties of estimators

Greene: Chapter 8: Endogeneity and Instrumental Variable Estimation

Pages 251-254: Natural experiments and the search for causal effects, and Summary and

Conclusions.

Kennedy: Chapter 1; Chapter 2, Section 2.1 - 2.8

Dowd, Bryan E. “Separated at Birth: Statisticians, Social Scientists and Causality in Health

Services Research,” Health Services Research (April 2011). Also see accompanying

commentaries by Judea Pearl and James O’Malley.

Holland, Paul. “Statistics and Causal Inference.” Journal of the American Statistical Association

81:396 (December 1986) 945-960.

Whitehouse, Mark. “Is an Economist Qualified to Solve Puzzle of Autism,” Wall Street Journal

(February 27, 2007). http://bpp.wharton.upenn.edu/mawhite/ReadingsFor911/11%20-

%20Whitehouse%20-%20WSJ%202-27-07%20-%20IV%20Methods%20and%20Autism.pdf

Supplementary:

Pearl, Judea. “Statistics and Causal Inference: A Review,” Sociedad de Estadistica e

Investigacion Operativa Test 12:2 (2003) 101-165. ftp://ftp.cs.ucla.edu/pub/stat_ser/R282-test-

prelim.pdf

3 Violation of OLS assumptions: Overview and Non-normal errors

Overview

Greene: Chapter 2 – The Linear Regression Model

Non-normal errors

Greene: pp.127-131: Non-normal disturbances and large sample tests; p.277: Weighted least squares

Kennedy: Chapters 3 and 15

Dowd, Bryan E., Swenson, Tami, Kane, Robert, Parashurma, Shriram, and Robert Coulam.

“Can Data Envelopment Analysis Provide a Scalar Index of Value?” Published “early view”

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online in Health Economics (2013).

Halvorsen, Robert and Raymond Palmquist. "The Interpretation of Dummy Variables in

Semilogarithmic Equations," American Economic Review 70:3 (June 1980) 474-475.

Kennedy, Peter. "Estimation with Correctly Interpreted Dummy Variables in Semilogarithmic

Equation, American Economic Review 71:4 (September 1981) 801.

Manning, W.G. and John Mullahy. “Estimating Log Models: To Transform or Not to Transform,”

Journal of Health Economics 20 (2001) 461-494.

4 Violation of OLS Assumptions: Variable Specification and Functional form

Greene: Chapters 5: Hypothesis Tests and Model Selection

Chapter 6: Functional Form and Structural Change

Kennedy: Chapters 4 to 6

5 Violation of OLS Assumptions: Heteroscedasticity and generalized linear regression

Greene: Chapter 9: The Generalized Regression Model and Heteroscedasticity

Kennedy: Chapters 7 and 8

6 Violation of OLS Assumptions: Autocorrelation

Greene: Chapter 20.7 – 20.9 Testing for autocorrelation; Efficient estimation when Ω is known;

Estimation when Ω is unknown.

Kennedy: Chapter 8

7 Analysis of panel data: Fixed versus random effect models, and difference-in-

differences

Greene: pp.156-158: Difference-in-Differences

Chapter 11: Models for panel data

Kennedy: Chapters 18 and 19

Bertrand, Marianne, Duflo Esther, and Sendhil Mullainathan. “How Much Should We Trust

Differences-in Differences Estimates?” Working Paper 8841 http://www.nber.org/papers/w8841

National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA 02138

(March 2002). Also in the Quarterly Journal of Economics,119:1 (February 2004) 249-275

8,9 Violation of OLS Assumptions: Missing Data, Measurement Error and

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Stochastic Regressors

Missing Observations

Greene: Section 4.7.4: Missing values and data imputation

Stochastic Regressors: Measurement Error

Greene: Section 4.7.5: Measurement error

Kennedy: Chapter 10

Stochastic Regressors: Lagged Dependent Variables

Greene: Section 20.7.3-4 Testing in the presence of a lagged dependent variable; 20.9.3 Estimation

with a lagged dependent variable

Kennedy: Chapter 10

10-11 Estimation

Greene: Chapters 12: Estimation Frameworks in Econometrics

Chapter 13: Minimum Distance Estimation and the Generalized Method of Moments

Chapter 14: Maximum Likelihood Estimation

Kennedy: Section 2.9, Chapter 14

12 Non-linear optimization methods

Greene: Appendix E

13-14 Discrete dependent variables: Binary logit/probit and interaction terms in non-linear

models, standard errors of non-linear functions

Greene: Chapter 17: Discrete Choice

Kennedy: Chapter 16

Karaca-Mandic, Pinar, Norton, Edward C. and Bryan E. Dowd “Interaction terms in non-linear

models,” Health Services Research (2011) (posted online).

Greene, William. “Testing hypotheses about interaction terms in non-linear models,” Economic

Letters 107 (2010) 291-296.

Dowd, Bryan E., Greene, William H., and Edward C. Norton. “Computation of Standard

Errors,” Health Services Research 49:2 (April 2014)731-750.

Supplementary:

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McFadden, Daniel. "A Comment on Discriminant Analysis "versus" Logit Analysis," Annals of

Economic and Social Measurement 5:4 (1976).

15-16 Polychotomous dependent variables: Multinomial/conditional Logit; Multinomial

probit; Ordered logit/probit.

Greene: Chapters 17: Discrete Choice

Kennedy: Chapter 16

Feldman, Roger, Finch, Michael, Dowd, Bryan and Steve Cassou. "The Elasticity of Demand for

Health Insurance Plans," Journal of Human Resources 24:1 (Winter, 1989) 115-142.

17-19 Limited dependent variables: Tobit and two-part Models

Greene: Chapter 18: Discrete Choices and Event Counts

Kennedy: Chapter 17:

Supplementary:

Manning, W.G. “The logged dependent variable, heteroskedasticity, and the retransformation

problem,” Journal of Health Economics 17:3 (1988) 283-296.

Mullahy, J. “Much Ado about Two: Reconsidering Retransformation and the Two-part Model in

Health Econometrtics,” Journal of Health Economics 17:3 (1988) 2826.

Buntin, Melinda Beeuwkes and Alan M. Zaslavsky. “Too Much Ado About Two-Part Models and

Transformation? Comparing Methods of Modeling Medicare Expenditures,” Journal of Health

Economics 23 (2004) 525-542.

20-24 Endogenous Explanatory Variables: Sample selection, Instrumental Variable Models,

Natural Experiments, Residual Inclusion, Propensity scores

Greene: Section 10.6: Simultaneous equation methods

19.5: Incidental truncation and sample selection

19.6: Evaluating treatment effects

Kennedy: Chapter 9

Dowd and Town, “Does X Really Cause Y?” (http://hcfo.net/pdf/xy.pdf)

Nichols, Austin. “Causal Inference with Observational Data,” Stata Journal 7:4 (2007) 507-541.

Bhattacharya J, Goldman D, McCaffrey D. 2006. Estimating probit models with self-selected

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treatments. Statistics in Medicine 25: 389-413.

Basu, Anirban. “Estimating Person-Centered Treatment (PeT) Effects using Instrumental

Variables: An Application to Evaluate Prostate Cancer Treatments,” Journal of Applied

Econometrics (2013) (online at http://onlinelibrary.wiley.com/doi/10.1002/jae.2343/full)

Terza, Joseph V., Bradford, W. David, and Clara E. Dismuke. “The Use of Linear Instrumental

Variable Methods in Health Services Research and Health Economics: A Cautionary Note,” Health

Services Research 43:3 (June 2008) 1002-1120.

Stukel, Therese A., Fisher, Elliot S., Wennberg, David E., Alter, David A., Gottlieb, Daniel J. and

Marina J. Vermeulen. “Analysis of Observational Studies in the Presence of Treatment Selection

Bias: Effects of Invasive Cardiac Management on AMI Survival Using Propensity Score and

Instrumental Variable Methods,” Journal of the American Medical Association 297:3 (January 17,

2007) 278-285.

Baiocchi, Mike, Small, Dylan, Yang, Lin, Polsky, Daniel, and Peter W. Groeneveld. “Near/Far

Matching – A Study Design Approach to Instrumental Variables,” Stanford University:

Department of Statistics.

http://www.google.com/url?sa=t&rct=j&q=near%2Ffar%20matching&source=web&cd=2&ved=

0CCkQFjAB&url=http%3A%2F%2Fwww-

stat.wharton.upenn.edu%2F~dsmall%2Fnearfarpaper.docx&ei=f-

s3UN2uMcqFyQG3o4HIBw&usg=AFQjCNFRbhzm2gEeuyoD-gK9KpvjwC0BNQ&cad=rja

Harris, Katherine and D.K. Remler. “Who is the Marginal Patient?” Health Services Research 33:5

Part 1, (December 1998) 1337-1360.

Imbens, Guido W. and Donald B. Rubin. “Estimating Outcome Distributions for Compliers in

Instrumental Variables Models,” Review of Economic Studies 64 (1997) 555-574.

Optional:

Angrist, Joshua E. and Jorn-Steffen Pischke. “The Credibility Revolution in Empirical Economics:

How Better Research Design is Taking the Con out of Econometrics,” Journal of Economic

Perspectives 24:2 (Spring 2010) 3-30. Be sure to read the commentaries by other authors that follow

this article in the same journal.

Heckman, James J. “Building Bridges Between Structural and Program Evaluation Approaches to

Evaluating Policy,” Journal of Economic Literature 48:2 (June 2010) 356-398. And Heckman,

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James J. and V. Joseph Hotz. “Choosing Among Alternative Nonexperimental Methods for

Estimating the Impact of Social Programs: The Case of Manpower Training,” Journal of the

American Statistical Association 84:408 (1989) 862-874.

Duan, Naihua, Manning, Willard G., Jr., Morris, Carl N. and Joseph P. Newhouse. "A Comparison

of Alternative Models for the Demand of Medical Care," Journal of Business and Economic

Statistics 1:2 (April 1983) 115-126.

Hay, Joel W. and Olsen, Randall J. "Let Them Eat Cake: A Note on Comparing Alternative Models

of the Demand for Medical Care," Journal of Business and Economic Statistics 2:3 (July 1984)

279-282.

Duan, Naihua, Manning, Willard G., Jr., Morris, Carl N. and Joseph P. Newhouse. "Choosing

Between the Sample-Selection Model and the Multi-Part Model," Journal of Business and Economic

Statistics 2:3 (July 1984) 283-289.

Nawata, Kazumitsu. “Estimation of Sample Selection Bias Models By the Maximum Likelihood

Estimator and Heckman’s Two-Step Estimator,” Economic Letters 45 (1994) 33-40.

Maddala G.S. "A Survey of the Literature on Selectivity Bias as it Pertains to Health Care Markets,"

in Advances in Health Economics and Health Services Research, Richard Scheffler and Louis

Rossiter, eds. JAI Press:Greenwich, CT (1984).

Manning, W.G., Duan, N. and W.H. Rogers. "Monte Carlo Evidence on the Choice Between

Sample Selection and Two-Part Models," Journal of Econometrics 35 (1987) 59-82.

Dowd, Bryan, Feldman, Roger, Cassou, Steve and Michael Finch. "Health Plan Choice and the

Utilization of Health Care Services," Review of Economics and Statistics 73:1 (February, 1991) 85-

93.

Leamer, Edward E. "Let's take the Con out of Econometrics," American Economic Review 73:1

(March 1983) 31-43.

Vella, Francis. “Estimating Models with Sample Selection Bias: A Survey,” The Journal of Human

Resources 33:1 127-169.

25-28 Endogenous Explanatory Variables: Simultaneous equations

Greene: Chapter 8: Endogeneity and Instrumental Variable Estimation

Kennedy: Chapter 11

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Supplementary:

Luft, Harold S., Hunt, Sandra S. and Susan C. Maerki. "The Volume-Outcome Relationship:

Practice-Makes-Perfect or Selective-Referral Patterns?" Health Services Research 22:2 (June 1987)

157-182.

McClellan, M. B., McNeil, B. and J. Newhouse. "Does More Intensive Treatment of Acute

Myocardial Infarction in the Elderly Reduce Mortality," Journal of the American Medical

Association (September 1994) 859-893.

McLaughlin, Catherine G. "HMO Growth and Hospital Expenses and Use: A Simultaneous

Equation Approach," Health Services Research 22:2 (June 1987) 183-205.

Optional

Heckman, James L. “Causal Parameters and Policy Analysis in Economics: A Twentieth Century

Retrospective,” NBER Working Paper 7333 (September 1999).

29-30 Analysis of duration data

Greene: Section 19.4: Models for Duration

Kennedy: 17. 4

Supplementary:

Keifer, Nicholas M. “Economic Duration Data and Hazard Functions,” Journal of Economic

Literature 26:2 (June 1988) 646-679.

Amemiya, Takeshi. Advanced Econometrics. Section 11.2

Welch, W. P. "HMO Enrollment and Medicaid: Survival Analysis with a Weibull Function,"

Medical Care 26:1 (January 1988) 45-52.

Additional topics (time permitting)

Count data

Greene: 18.4: Models for counts of events

Regression discontinuity

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Greene: 19.6.3: Regression discontinuity

Cook, T.D. “Waiting for Life to Arrive”: A history of the regression-discontinuity design in

Psychology, Statistics and Economics. Journal of Econometrics 142:2 (2008) 636-654.

Card, David, Dobkin, Carlos, and Nicole Maestas. “Does Medicare Save Lives?” Quarterly

Journal of Economics 124:2 (May 2009) 597-636.

Supplementary:

Lee, David S. and Thomas Lemieux. Egression Discontinuity Designs in Economics,” Journal

of Economic Literature 48:2 (281-355).

Imbens, Guido and Thomas Lemieux. Regression Discontinuity Designs: A Guide to Practice,”

Technical Working Paper 337 http://www.nber.org/papers/t0337 National Bureau of Economic

Research, 1050 Massachusetts Avenue, Cambridge, MA 02138, (April 2007).

Microsimulation

Atherly, Adam and Bryan E. Dowd. “Should Health Medicare Beneficiaries Postpone

Enrollment in Part D,” Health Economics 18 (2009) 921–931.

http://onlinelibrary.wiley.com/doi/10.1002/hec.1413/pdf

Joyce, G.F., Keeler, E.B., et al. (2005). “The Lifetime Burden of Chronic Disease Among the

Elderly.” Health Affairs Web Exclusive 5:R18-R29.*

Spielauer, M. (2011) “What is Social Science Microsimulation?” Social Science Computer

Review 29(1):9-20.*

Matching and Linking data

Disability/Accessibility Statement

1) It is University policy to provide, on a flexible and individualized basis, reasonable

accommodations to students who have documented disability conditions (e.g., physical, learning,

psychiatric, vision, hearing, or systemic) that may affect their ability to participate in course

activities or to meet course requirements. Students with disabilities are encouraged to contact

Disability Services for a confidential discussion of their individual needs for accommodations.

Disability Services is located in Suite 180 McNamara Alumni Center, 200 Oak Street. Staff can

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be reached by calling 612/626-1333 voice or TTY. The website is

http://disserv3.stu.umn.edu/index2.html.

2) Letter grades will be determined by total effort as follows:

A = 95-100 points

A- = 90-94 points

B+ = 87-89 points

B = 83-86 points

B- = 80-82 points

C+ = 77-79 points

C = 73-76 points

C- = 70-72 points

F (or N) – Represents failure (or no credit) and signifies that the work was either (1)

completed but at a level of achievement that is not worthy of credit or (2) was not completed and

there was no agreement between the instructor and the student that the student would be awarded

an I.

S – Achievement that is satisfactory will be expected to complete all assignments and receive a

minimum of 70% to receive a passing scores (achievement required for an S is at the discretion

of the instructor but may be no lower than a 70%).

I – An incomplete grade ("I") is permitted only in cases of exceptional circumstances and

following consultation with the instructor. In such cases, an "I" grade will require a specific

written agreement between the instructor and student specifying the time and manner in which

the student will complete the course requirements. Extension for completion of the work will not

exceed one year. Additionally, some majors in the School of Public Health may place a hold on

a student’s registration until an “I” is cleared up, or a plan for completion of the work submitted.

3) Students may change grading options without written permission as specified by the

University and without penalty during the initial registration period or during the first two weeks

of the semester. The grading option may not be changed after the second week of the term.

4) School of Public Health students may withdraw from a course through the second week

of the semester without permission. No “W” will appear on the transcript.

After the second week students are required to do the following:

1. The student must contact and notify their advisor and course instructor informing them of

the decision to withdraw from the course.

2. An email must be sent to the SPH Student Services Center (SSC)

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([email protected]) from the student. The email must provide name, ID#, course

number, section number, semester and year with instructions to withdraw from the

course, and acknowledgement that the instructor and advisor have been contacted.

3. The advisor and instructor must email the SSC acknowledging the student is canceling

the course. All parties must be notified of the student’s intent.

4. After the SSC receives all emails (student, advisor, instructor) the SSC will complete the

process by withdrawing the student from the course. A “W” will be placed and remain on

the student transcript.

After discussion with their advisor and notification to the instructor, students may withdraw up

until the eighth week of the semester. There is no appeal process. For more information, contact

the SPH Student Services Center at 612.626.3500.

A refund schedule for tuition and fees is listed in the University class schedule. Please refer to

these dates when withdrawing from courses.

5) Scholastic dishonesty is a violation of the student conduct code and is defined as “any act

that violates the rights of another student in academic work or that involves misrepresentation of

your own work. Scholastic dishonesty includes (but is not limited to): cheating on assignments

or examinations; plagiarizing, which means misrepresenting as your own work any part of work

done by another; submitting the same paper, or substantially similar papers, to meet the

requirements of more than one course without the approval and consent of all instructors

involved; depriving another student of necessary course materials; or interfering with another

student’s work.” Scholastic dishonesty in any portion of the academic work for a course shall be

grounds for awarding a grade of “F” or “N” for the entire course. Please consult the student

conduct code at: http://www1.umn.edu/regents/policies/academic/StudentConduct.html