A comparison of treatment effects estimators using a structural model of AMI treatment choices and severity of illness information from hospital charts

Journal of Applied Econometrics

Volumn 26, Issue 5, 2011, Pages 825-853

  Khwaja, Ahmed ^a   Picone, Gabriel ^b   Salm, Martin ^c   Trogdon, Justin G ^d  

^a DUKE UNIVERSITY   (United States)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

^c TILBURG UNIVERSITY   (Netherlands)

^d RTI INTERNATIONAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79960440153     PISSN: 08837252     EISSN: 10991255     Source Type: Journal    
DOI: 10.1002/jae.1181     Document Type: Article

References (41)

1. Aakvik A, Heckman J, Vytlacil E. 2005. Estimating treatment effects for discrete outcomes when response to treatment vary: an application to Norwegian vocational rehabilitation programs. Journal of Econometrics 125: 15-51.
2. Abadie A, Imbens G. 2009. On the failure of bootstrap for matching estimators. Econometrica 76: 1537-1557.
3. Angrist JD, Krueger AB. 1999. Empirical strategies in labor economics. In Handbook of Labor Economics, Vol. 3, Ashenfelter O, Card D (eds). Elsevier: Amsterdam; 1277-1366.
4. Arcidiacono P, Jones JB. 2003. Finite mixture distributions, sequential likelihood and the EM algorithm. Econometrica 71: 933-946.
5. Arcidiacono P, Khwaja A, Ouyang L. 2007. Habit persistence and teen sex: could increased access to contraception have unintended consequences for teen pregnancies? Mimeo, Department of Economics, Duke University.
6. Barnow B, Cain G, Goldberg A. 1980. Issues in the analysis of selectivity bias. Evaluation Studies 5: 42-59.
7. Bound J, Jaeger D, Baker R. 1995. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association 90: 443-450.
8. Cameron AC, Trivedi PK. 2005. Microeconometrics: Methods and Applications. Cambridge University Press: New York.
9. Charlson ME, Pompei P, Ales KL, MacKenzie CR. 1987. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases 40: 373-383.
10. Cobb-Clark D, Crossley T. 2003. Econometrics for evaluation: an introduction to recent developments. Economic Record 79: 491-511.
11. Dehejia R, Wahba S. 1999. Causal effects in nonexperimental studies: reevaluating the evaluation training programs. Journal of the American Statistical Association 94: 1053-1062.
12. Gowrisankaran G, Town R. 1999. Estimating the quality of care in hospitals using instrumental variables. Journal of Health Economics 18: 747-767.
13. Hamilton B, Hamilton VH. 1998. Estimating surgical volume-outcome relationships applying survival models: accounting for frailty and hospital fixed effects. Health Economics 6: 383-395.
14. Heckman JJ, Singer B. 1984. A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52(2): 271-320.
15. Heckman J, Vytlacil E. 2005. Structural equations, treatment effects, and econometric policy evaluation. Econometrica 73: 669-738.
16. Heckman J, Vytlacil E. 2007a. Econometric evaluation of social programs. Part I: Causal models, structural models, and econometric policy evaluation. In Handbook of Econometrics, Vol 6B, Heckman J, Leamer E (eds). Elsevier: Amsterdam; ch. 70.
17. Heckman J, Vytlacil E. 2007b. Econometric evaluation of social programs. Part II: Using the marginal treatment effect to organize alternative econometric estimators to evaluate social programs, and to forecast their effects in new environments. In Handbook of Econometrics, Vol 6B, Heckman J, Leamer E (eds). Elsevier: Amsterdam; ch. 71.
18. Heckman J, Ichimura H, Todd P. 1997. Matching as an econometric evaluation estimator: evidence from evaluating a job training program. Review of Economic Studies 64: 605-654.
19. Heckman J, Ichimura H, Todd P. 1998. Matching as an econometric evaluation estimator. Review of Economic Studies 65: 261-294.
20. Heckman J, LaLonde R, Smith J. 1999. The economics and econometrics of active labor market programs. In Handbook of Labor Economics, Vol 3, Ashenfelter O, Card D (eds). Elsevier: Amsterdam; 1865-2097.
21. Ho V. 2002. Learning and the evolution of medical technologies: the diffusion of coronary angioplasty. Journal of Health Economics 21: 873-885.
22. Imbens G. 2004. Nonparametric estimation of average treatment effects under exogeneity: a review. Review of Economics and Statistics 86(1): 4-29.
23. Imbens G, Angrist JD. 1994. Identification and estimation of local average treatment effects. Econometrica 62(2): 467-475.
24. Khwaja A. 2001. Health insurance, habits and health outcomes: a dynamic stochastic model of investment in health. PhD dissertation, University of Minnesota.
25. Killip T, Kimbal JT. 1967. Treatment of myocardial infarction in a coronary care unit. American Journal of Cardiology 20: 457-464.
26. Krumholz HM, Chen J, Chen YT, Wang Y, Radford MJ. 2001. Predicting one-year mortality among elderly survivors of hospitalization for an acute myocardial infarction: results from the Cooperative Cardiovascular Project. Journal of the American College of Cardiology 38: 453-459.
27. LaLonde RJ. 1986. Evaluating the econometric evaluations of training programs with experimental data. American Economic Review 76: 604-620.
28. Luft HS, Garnick D, Mark D, McPhea S. 1990. Hospital Volume, Physician Volume and Patient Outcomes: Assessing the Evidence. Health Administration Press: Ann Arbor, MI.
29. Marciniak TA, Ellerbeck EF, Radford MJ, Kresowik TF, Gold JA, Krumholz HM, Kiefe CI, Allman RM, Vogel RA, Jencks SF. 1998. Improving the quality of care for Medicare patients with acute myocardial infarction: results from the Cooperative Cardiovascular Project. Journal of the American Medical Association 287: 1269-1276.
30. McClellan M, Newhouse JP. 1997. The marginal cost-effectiveness of medical technology: a panel instrumental-variables approach. Journal of Econometrics 77: 39-64.
31. McClellan M, Noguchi H. 2001. Validity and interpretation of treatment effect estimates: using observational data. Stanford University.
32. McClellan M, McNeil B, Newhouse JP. 1994. Does more intensive treatment of acute myocardial infarction reduce mortality? Journal of the American Medical Association 272: 859-866.
33. McFadden D. 1981. Econometric models of probabilistic choice. In Structural Analysis of Discrete Data with Econometric Applications, Manski C, McFadden D (eds). MIT Press: Cambridge, MA; 198-272.
34. Michalopoulos C, Bloom HS, Hill CJ. 2002. Can propensity-score methods match the findings from a random assignment evaluation of mandatory welfare-to-work programs? Review of Economics and Statistics 86(1): 156-179.
35. Rosenbaum P, Rubin D. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70(1): 41-55.
36. Rust J. 1994. Structural estimation of Markov decision processes. In Handbook of Econometrics, Vol 4, Engle RF, McFadden DL (eds). Elsevier: Amsterdam; 3081-3143.
37. Rust J, Phelan C. 1997. How Social Security and Medicare affect retirement behavior in a world of incomplete markets. Econometrica 65: 781-831.
38. Sloan FA, Picone G, Taylor D, Chou S. 2001. Hospital ownership and cost quality of care: is there a dime's worth of difference? Journal of Health Economics 20: 1-21.
39. Staiger D, Stock J. 1997. Instrumental variables estimation with weak instruments. Econometrica 65: 557-586.
40. Trogdon JG. 2009. Demand for and regulation of cardiac services. International Economic Review 50(4): 1183-1204.
41. Wooldridge J. 2001. Econometrics Analysis of Cross Section and Panel Data. MIT Press: Cambridge, MA.