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.

CONTEXT Comparisons of outcomes between patients treated and untreated in observational studies may be biased due to differences in patient prognosis between groups, often because of unobserved treatment selection biases. OBJECTIVE To compare 4 analytic methods for removing the effects of selection bias in observational studies: multivariable model risk adjustment, propensity score risk adjustment, propensity-based matching, and instrumental variable analysis. DESIGN, SETTING, AND PATIENTS A national cohort of 122,124 patients who were elderly (aged 65-84 years), receiving Medicare, and hospitalized with acute myocardial infarction (AMI) in 1994-1995, and who were eligible for cardiac catheterization. Baseline chart reviews were taken from the Cooperative Cardiovascular Project and linked to Medicare health administrative data to provide a rich set of prognostic variables. Patients were followed up for 7 years through December 31, 2001, to assess the association between long-term survival and cardiac catheterization within 30 days of hospital admission. MAIN OUTCOME MEASURE Risk-adjusted relative mortality rate using each of the analytic methods. RESULTS Patients who received cardiac catheterization (n = 73 238) were younger and had lower AMI severity than those who did not. After adjustment for prognostic factors by using standard statistical risk-adjustment methods, cardiac catheterization was associated with a 50% relative decrease in mortality (for multivariable model risk adjustment: adjusted relative risk [RR], 0.51; 95% confidence interval [CI], 0.50-0.52; for propensity score risk adjustment: adjusted RR, 0.54; 95% CI, 0.53-0.55; and for propensity-based matching: adjusted RR, 0.54; 95% CI, 0.52-0.56). Using regional catheterization rate as an instrument, instrumental variable analysis showed a 16% relative decrease in mortality (adjusted RR, 0.84; 95% CI, 0.79-0.90). The survival benefits of routine invasive care from randomized clinical trials are between 8% and 21%. CONCLUSIONS Estimates of the observational association of cardiac catheterization with long-term AMI mortality are highly sensitive to analytic method. All standard risk-adjustment methods have the same limitations regarding removal of unmeasured treatment selection biases. Compared with standard modeling, instrumental variable analysis may produce less biased estimates of treatment effects, but is more suited to answering policy questions than specific clinical questions.

[1]  E. Fisher,et al.  The relation between the availability of neonatal intensive care and neonatal mortality. , 2002, The New England journal of medicine.

[2]  A. Laupacis,et al.  Cyclo-oxygenase-2 inhibitors versus non-selective non-steroidal anti-inflammatory drugs and congestive heart failure outcomes in elderly patients: a population-based cohort study , 2004, The Lancet.

[3]  D. Cox,et al.  A General Definition of Residuals , 1968 .

[4]  Harlan M. Krumholz,et al.  Improving the Quality of Care for Medicare Patients with Acute Myocardial Infarction: Results from the Cooperative Cardiovascular Project , 1999 .

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

[6]  P. Austin,et al.  The use of the propensity score for estimating treatment effects: administrative versus clinical data , 2005, Statistics in medicine.

[7]  S. Yusuf,et al.  Thrombolytic therapy for eligible elderly patients with acute myocardial infarction. , 2005, American heart journal.

[8]  Jeffrey M. Wooldridge,et al.  Introductory Econometrics: A Modern Approach , 1999 .

[9]  D. Lin,et al.  Cox regression analysis of multivariate failure time data: the marginal approach. , 1994, Statistics in medicine.

[10]  Joseph P. Newhouse,et al.  Does More Intensive Treatment of Acute Myocardial Infarction in the Elderly Reduce Mortality? Analysis Using Instrumental Variables , 1995 .

[11]  David A. Schoenfeld,et al.  Partial residuals for the proportional hazards regression model , 1982 .

[12]  Therese A. Stukel,et al.  Long-term Outcomes of Regional Variations in Intensity of Invasive vs Medical Management of Medicare Patients With Acute Myocardial Infarction , 2005 .

[13]  Lori S. Parsons Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques , 2001 .

[14]  Lipid-lowering therapy with statins in high-risk elderly patients: the treatment-risk paradox. , 2004 .

[15]  J. Newhouse,et al.  Econometrics in outcomes research: the use of instrumental variables. , 1998, Annual review of public health.

[16]  Sunil J Rao,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .

[17]  J. Stock,et al.  Instrumental Variables Regression with Weak Instruments , 1994 .

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

[19]  A. Laupacis,et al.  Risk-treatment mismatch in the pharmacotherapy of heart failure. , 2005, JAMA.

[20]  M. Landrum,et al.  Causal Effect of Ambulatory Specialty Care on Mortality Following Myocardial Infarction: A Comparison of Propensity Score and Instrumental Variable Analyses , 2001, Health Services and Outcomes Research Methodology.

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

[22]  J. Boura,et al.  Primary angioplasty versus intravenous thrombolytic therapy for acute myocardial infarction : a quantitative review of 23 randomised trials , 2022 .

[23]  Peter C Austin,et al.  Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. , 2005, Journal of clinical epidemiology.

[24]  David Wennberg,et al.  The Implications of Regional Variations in Medicare Spending. Part 2: Health Outcomes and Satisfaction with Care , 2003, Annals of Internal Medicine.

[25]  M A Hlatky,et al.  Variation among hospitals in coronary-angiography practices and outcomes after myocardial infarction in a large health maintenance organization. , 1996, The New England journal of medicine.

[26]  S. Yusuf,et al.  Routine vs selective invasive strategies in patients with acute coronary syndromes: a collaborative meta-analysis of randomized trials. , 2005, JAMA.

[27]  P D Cleary,et al.  Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. , 2001, Journal of clinical epidemiology.

[28]  J. Avorn,et al.  Risk of death in elderly users of conventional vs. atypical antipsychotic medications. , 2005, The New England journal of medicine.

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

[30]  D. Cox,et al.  Analysis of Survival Data. , 1986 .

[31]  P. Rosenbaum Discussing hidden bias in observational studies. , 1991, Annals of internal medicine.

[32]  R. Califf,et al.  1999 update: ACC/AHA guidelines for the management of patients with acute myocardial infarction. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Management of Acute Myocardial Infarction). , 1996, Journal of the American College of Cardiology.

[33]  David Wennberg,et al.  The Implications of Regional Variations in Medicare Spending. Part 1: The Content, Quality, and Accessibility of Care , 2003, Annals of Internal Medicine.

[34]  A. Hartz,et al.  A comparison of observational studies and randomized, controlled trials , 2000, American journal of ophthalmology.

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

[36]  J. Concato,et al.  Randomized, controlled trials, observational studies, and the hierarchy of research designs. , 2000, The New England journal of medicine.

[37]  Paul R Rosenbaum,et al.  Rare Outcomes, Common Treatments: Analytic Strategies Using Propensity Scores , 2002, Annals of Internal Medicine.

[38]  David P Miller,et al.  Determinants of the use of coronary angiography and revascularization after thrombolysis for acute myocardial infarction. , 1996, The New England journal of medicine.

[39]  D. Juurlink,et al.  Heart failure/transplant: abstractsCyclo-oxy genase-2 inhibitors versus nonselective, nonsteroidal anti-inflammatory drugs and congestive heart failure outcomes in elderly patients: A population-based cohort study☆ , 2004 .

[40]  R. D'Agostino Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. , 2005, Statistics in medicine.