Causal Effect of Ambulatory Specialty Care on Mortality Following Myocardial Infarction: A Comparison of Propensity Score and Instrumental Variable Analyses

[1]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[2]  Gautam Tripathi,et al.  ECONOMETRIC METHODS , 2000, Econometric Theory.

[3]  N. Powe,et al.  The association between hospital volume and survival after acute myocardial infarction in elderly patients. , 1999, The New England journal of medicine.

[4]  R. D'Agostino.  Adjustment Methods: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non‐Randomized Control Group , 2005 .

[5]  D. Rubin,et al.  The bias due to incomplete matching. , 1983, Biometrics.

[6]  J. Angrist,et al.  Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity , 1995 .

[7]  Joshua D. Angrist,et al.  Identification of Causal Effects Using Instrumental Variables , 1993 .

[8]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[9]  H. Krumholz,et al.  Improving the quality of care for Medicare patients with acute myocardial infarction: results from the Cooperative Cardiovascular Project. , 1998, JAMA.

[10]  B J McNeil,et al.  Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. , 1994, JAMA.

[11]  D. Rubin,et al.  Assessing the effect of an influenza vaccine in an encouragement design. , 2000, Biostatistics.

[12]  J. Angrist,et al.  Identification and Estimation of Local Average Treatment Effects , 1994 .

[13]  D. Rubin,et al.  Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .

[14]  P. Holland.  Statistics and Causal Inference , 1985 .

[15]  L. Goldman,et al.  The effectiveness of right heart catheterization in the initial care of critically ill patients. SUPPORT Investigators. , 1996, JAMA.

[16]  C. Eaton,et al.  Outcome of acute myocardial infarction according to the specialty of the admitting physician. , 1997, The New England journal of medicine.

[17]  D. Rubin,et al.  Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome , 1983 .

[18]  V. Fuster,et al.  Do cardiologists do it better? , 1997, Journal of the American College of Cardiology.

[19]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects , 2005 .

[20]  D. Rubin,et al.  Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies , 1978 .

[21]  M. Mcclellan,et al.  Does physician specialty affect the survival of elderly patients with myocardial infarction? , 2000, Health services research.

[22]  E. Cook,et al.  Right heart catheterization and cardiac complications in patients undergoing noncardiac surgery: an observational study. , 2001, JAMA.

[23]  C. Glymour,et al.  STATISTICS AND CAUSAL INFERENCE , 1985 .

[24]  R H Brook,et al.  Effect of physician specialty on use of necessary coronary angiography. , 1995, Journal of the American College of Cardiology.

[25]  K. Harris,et al.  Who is the marginal patient? Understanding instrumental variables estimates of treatment effects. , 1998, Health services research.

[26]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[27]  B. McNeil,et al.  Treatment and outcomes of acute myocardial infarction among patients of cardiologists and generalist physicians. , 1997, Archives of internal medicine.

[28]  William A. Knaus,et al.  The effectiveness of right heart catheterization in the initial care of critically ill patients. SUPPORT Investigators. , 1996, Journal of the American Medical Association (JAMA).