# Propensity Scores in Cardiovascular Research

## Jump to

Propensity scores have been used to reduce bias in observational studies in many fields and are becoming more widely used in cardiovascular research.^{1} The goal of this statistical primer is to present the definition of propensity scores and to illustrate their use by describing some recent examples found in the cardiovascular disease research literature.

Large-scale epidemiological cohort studies such as the Multi-Ethnic Study of Atherosclerosis (MESA)^{2} are designed to follow a large sample of participants over time without active administration of any interventions. Within MESA, lack of randomization can complicate potential treatment comparisons such as the impact of β-blocker versus angiotensin-converting enzyme inhibitor usage. Nonrandomized comparisons may also arise from within a randomized clinical trial. For instance, the Clopidogrel as Adjunctive Reperfusion Therapy - Thrombolysis in Myocardial Infarction 28 (CLARITY-TIMI 28) trial^{3} is a randomized study that compares clopidogrel with placebo in 3491 ST-elevation myocardial infarction patients aged 18 to 75 years who have undergone fibrinolysis. In addition to the primary end points, investigators wished to compare the effects of low molecular weight heparin with unfractionated heparin on angiographic and clinical outcomes in participants.^{4} These treatments were not randomly assigned.

In studies such as these, the treatment groups may markedly differ with respect to the observed pretreatment covariates measured on participants. These differences could lead to biased estimates of treatment effects. The propensity score for an individual, defined as the conditional probability of being treated given the individual’s covariates, can be used to balance the covariates in the 2 groups and thus reduce this bias.

In a randomized experiment, the randomization of participants to different treatments minimizes the chance of differences on observed or unobserved covariates. However, in nonrandomized studies, systematic differences can exist between treatment groups. To control for this potential bias, information on measured covariates can be incorporated into the study design (eg, through matched sampling) or into estimation of the treatment effect (eg, through stratification or covariance adjustment). However, such methods of adjustment can often use only a limited number of covariates, whereas adjustments that use propensity scores do not have this limitation.

A simple illustration of how an imbalance on covariates could influence a treatment effect estimate is as follows. Consider a nonrandomized study with 2 groups and a binary outcome with the data shown in the Table.

We can clearly see in the Table that gender is not balanced between the 2 groups (80% males in Group A versus 10% in Group B). The apparent treatment difference between groups would not be significant if we adjusted for gender. In other words, if we created balance between the 2 groups on the basis of gender, we would recognize that the apparent treatment effect is caused by gender and not group. In most observational studies, there may be several variables that are imbalanced at the same time between groups and the propensity score methodology allows one to simultaneously balance all the covariates and make more valid inferences about treatment effects.

## Definition

The propensity score for an individual is the probability of being treated conditionally on (or only on the basis of) the individual’s covariate values. Intuitively, the propensity score is a measure of the likelihood that a person would have been treated on the basis of only his or her covariate scores. The propensity score is the probability that a participant is in the “treated” group given his/her background (pretreatment) characteristics. This score is frequently estimated by logistic regression where the treatment variable (treated yes/no) is the outcome and the covariates are the predictor variables in the model.

The propensity score method can be used if conditional independence exists between the treatment assignment and potential outcomes given the covariates (referred to as strongly ignorable treatment assignment). In other words, the treatment assignment can be associated with covariate values but not be related to outcome values once the covariates are controlled for. The above is a description of the relationship of treatment *assignment* (eg, being placed in the β-blocker group) to covariates and outcomes, not a description of the relationship of the treatment effect (eg, the impact of taking β-blockers) to the covariates or outcomes. When the treatment assignment is strongly ignorable, as is most often the case, one can estimate the propensity score and use this score as a balancing score^{5} to “balance” the distribution of the covariates in the treated and control groups. Matching, stratification, or regression (covariance) adjustment with the propensity score can be used to produce unbiased estimates of the treatment effects and create covariate balance between groups. In some of these methods, the propensity score itself is used in the analyses as a weight or factor (regression adjustment), whereas in others it is used to construct the appropriate comparisons (stratification or matching) but not in the analyses directly.

In practice, the success of propensity score modeling is judged by whether balance on covariate values is achieved between the treatment groups after its use. Because of this, one can be more liberal with inclusion of covariates in the model than in most traditional settings. For instance, covariates with *P* values larger than 0.05 can be included in the propensity score model. One limitation that concerns the number of covariates that can be included in the model is that there needs to be a sufficient number of participants in each treatment group for each covariate that is included. For instance, if a study includes 30 treated and 50 untreated individuals, the propensity score model should have much less than 30 covariates included. Once the model is fit, one method to evaluate the success of a particular propensity score model is to compare the amount of bias (or imbalance) that existed on observed covariates in the treated and control groups before and after adjustment for propensity scores.

One advantage of propensity scores is that if 2 subjects are found, 1 subject in the treated group and 1 subject in the control, with the same propensity score, then one could imagine that these 2 subjects were “randomly” assigned to each group in the sense of being equally likely to be treated or control. Because propensity scores are estimated with only observed covariates, one has to assume that unobserved covariates would not have changed the model had they been measured. When this assumption is true, one can be fairly confident that approximately unbiased estimates for the treatment effect can be obtained.

When building the propensity score model, only covariates that occur pretreatment should be included. If one includes covariates that are measured posttreatment, then the propensity score model may explain part of the treatment effect itself. For example, if one wished to compare in an observational study the impact of a β-blocker versus an angiotensin-converting enzyme inhibitor, the propensity score model could include age, smoking status, and prior medical history. However, patient characteristics measured after the treatment began, such as an ejection fraction measurement taken posttreatment (eg, after β-blocker initiation) should not be included. Indeed, ejection fraction may indeed be imbalanced between the treatment groups; however, this imbalance may be caused by the treatment and therefore is part of the outcome.

## Uses of Propensity Scores

The 3 most common techniques that use the propensity score are matching, stratification, and regression adjustment. Each of these techniques is a way to make an adjustment for covariates before calculation of the treatment effect (matching and stratification) or during calculation of treatment effect (stratification and regression adjustment). With all 3 techniques, the propensity score is calculated in the same way, but once estimated it is applied differently. Propensity scores are useful for these techniques because by definition the propensity score is the conditional probability of treatment given the observed covariates; thus, subjects in treatment and control groups with equal (or nearly equal) propensity scores will tend to have the same (or nearly the same) distributions on their background covariates.^{6} Exact adjustments made with the propensity score will, on average, remove all the bias in the background covariates. Therefore, bias-removing adjustments can be made with the propensity scores rather than all the background covariates individually.

## Matching

An example of propensity score matching was described in a recently published study that examined whether hypothermic circulatory arrest (HCA) is a risk factor for neurologic morbidity in aortic surgery.^{7} More than 500 individuals (238 HCA+ and 273 HCA−) participated in this study. When the investigators first compared the groups, they determined that many characteristics were different between the 2 groups, such as gender, age, smoking history, and hypertension. Thus, they were concerned that if differences between HCA+ and HCA− were found, these could be the result of differences in pretreatment conditions only. To handle this, the investigators estimated propensity scores for all participants with 9 covariates and then matched HCA+ and HCA− participants with the propensity score. The maximum difference in the propensity score allowed for a match was 0.015, and with this criterion 220 closely matched patients (110 in each group) were identified for their final analyses. The investigators demonstrated that all baseline characteristics that had been significantly different (unbalanced) between groups in the overall study were balanced on the propensity-matched pairs. Thus, HCA comparisons could be made on this subgroup of participants.

Matching is a common technique used to select control subjects who are “matched” with the treated subjects on background covariates that the investigator believes need to be controlled. Although the idea of finding matches seems straightforward, it is often difficult to find subjects who are similar on all covariates, even when only a few background covariates of interest exist. The investigators for the HCA example above would have confronted this problem as they had identified 9 variables on which they wished to match subjects.

Propensity score matching solves this problem by allowing an investigator to control for many background covariates simultaneously by matching on a single variable, the propensity score. Propensity scores can be calculated with many covariates, and the result for each participant is a scalar summary (single number) of his/her covariates.

To evaluate the success of propensity score matching, a common technique is to compare covariates in the treated and control groups before and after matching. For continuous variables one can compare means or *t* statistics pre- and postmatching, and for categorical variables one can compare frequencies/percents and χ^{2} statistics pre- and postmatching. Estimates of the percent reduction in bias from propensity score matching can be found by calculation of an initial bias (as the difference in covariate mean values between the treated and control groups before matching, b_{i}) and the postmatching bias (as the difference in covariate mean values after matching, b_{m}) and then calculation of the percent reduction in bias as 100(1−b_{m}/b_{i}).

In many settings, propensity score matching can also be very cost-effective. In particular, if an investigator has access to a large database or patient population where the treatment indicator and background covariates have been measured, but outcomes of interest have not been measured yet, propensity score matching can be used to identify the appropriate subset of individuals from which to gather additional outcome measures rather than have data collected on all individuals.

## Stratification

Stratification is also commonly used in observational studies to control for systematic differences between the control and treated groups. This technique consists of grouping subjects into strata determined by observed background characteristics. Once the strata are defined, treated and control subjects who are in the same strata are compared directly. Many of the same problems occur in stratification as in matching when the number of covariates increases. As the number of covariates increases, the number of strata grows exponentially. For instance, if there were *k* dichotomous covariates to be controlled for, then there would need to be 2^{k} strata created. If *k* is large, then some strata might contain subjects from only 1 group, which would make it impossible to estimate a treatment effect in that stratum. Here again, the propensity score is very useful. Because the propensity score is a scalar summary of all the observed background covariates, stratification on it alone can improve the overall balance of the distributions of the covariates in the treated and control groups without the exponential increase in number of strata.

It has been shown that stratification based on the propensity score will produce strata where the average treatment effect within strata is an unbiased estimate of the true treatment effect.^{8} In addition, research has shown that creation of 5 strata (ie, by quintiles) can in general remove approximately 90% of the bias caused by strata variables (propensity score).^{9} In fact, stratification on the propensity score balances all covariates that are used to estimate the propensity score, and often 5 subclasses based on the propensity score will remove >90% of the bias in each of these covariates.

The technique used to determine strata is straightforward. Once the propensity score is estimated, the investigator must decide how many strata should be used. As stated above, 5 strata (ie, quintiles) are usually sufficient; however, the number of strata used depends on how many participants are available in the overall study. The strata boundaries are normally based on the values of the propensity score for both groups combined rather than on the treated or control group alone. A recent publication that used propensity scores for stratification examined whether excessive variation exists in providing coronary angiography to patients after acute myocardial infarction on the basis of chronic kidney disease and whether an association exists between angiography and mortality.^{10} The investigators estimated propensity scores for the probability of undergoing coronary angiography during hospitalization among 6794 chronic kidney disease patients who were rated appropriate for the procedure. Here the dependent variable (ie, treatment indicator) was provision of angiography, and the covariates used in the model included both patient level and hospital characteristics. Once propensity scores were estimated for all participants, the investigators ranked all appropriate chronic kidney disease patients by their estimated propensity scores and created quintiles based on these propensity scores. Analyses were then performed within each of the 5 strata to compare odds ratios and 95% confidence intervals for 1-year mortality for those who underwent coronary angiography versus those who did not. With this approach, all quintiles except the lowest (where the likelihood of angiography was <6%) showed that the odds of death were higher for those with no angiography. Although these results were similar to those found with an overall logistic regression, the investigators concluded, “Given that the propensity score approach requires fewer assumptions and tends to balance differences between treated and untreated groups, we prefer these results to those of the logistic regression model.”^{10}

## Regression (Covariance) Adjustment

Propensity scores can also be used in regression (covariance) adjustment. In regression adjustment, the treatment effect is estimated by adjustment for the impact of background covariates in a regression model. In general, covariance-adjusted models can contain 1 or more covariates. The propensity score is a useful variable in regression adjustments, because one can first fit a propensity score model that includes many potential covariates, and then the final treatment effect model only has to include the propensity score as a covariate to derive adjusted estimates.

Another approach to regression adjustment is to use a large set of background covariates to estimate the propensity score and then use a subset of these covariates and the propensity score in the regression adjustment. A recent article in the cardiovascular research literature examined whether mitral valve annuloplasty (MVA) improves long-term mortality in patients with mitral regurgitation and left ventricular systolic dysfunction in 419 patients felt to be candidates for MVA.^{11} To examine this question the investigators estimated propensity scores that predicted whether a patient would undergo MVA on the basis of demographics, physical examination findings, electrocardiography and echocardiography measurements, and medications that clinically would likely affect the probability of undergoing MVA. Once the propensity scores were estimated for each participant, Cox proportional hazards models were fit to examine the impact of MVA on event-free survival where the propensity score was forced into the model as a covariate. Additional models were fit that included the propensity score and other covariates, and the investigators found that final predicted values remained consistent with or without the propensity score as long as a subset of important covariates were included.

One question that may arise when regression adjustment with propensity scores is used is whether any gain results from the use of the propensity score rather than performance of a regression adjustment with all the covariates used to estimate the propensity score included in the model. Rubin^{12} showed that the results from both methods should often lead to the same conclusions as in the case in the MVA example above. However, one advantage to the 2-step procedure (with propensity scores) is that one can fit a very complicated propensity score model with interactions and higher order terms first. Because the goal of this propensity score model is to obtain the best estimated probability of treatment assignment, one is not concerned with over-parameterizing this model. Then when the model for the treatment effect estimation is fit, the investigator can include only a subset of the most important variables, such as the propensity score, in the model. This smaller model may allow the investigator to perform diagnostic checks on the fit of the model more reliably than if many covariates were included in the model.

One can combine the previous 2 techniques, stratification and regression adjustment, by first stratifying the data on the basis of the propensity score and then using regression adjustment with a subset of important covariates within each stratum. It has been suggested that this estimator of the treatment effect may be better than deriving the treatment effect with any of the 3 methods (matching, stratification, or regression adjustment) alone.

## Summary

Propensity scores are being widely used in statistical analyses, particularly in the area of cardiovascular disease research. Their use is likely to continue to increase as the cost for randomized clinical trials rises and more investigators turn to observational studies as a method of research. The propensity score methodology appears to produce the greatest benefits when it can be incorporated into the design stages of studies (through matching or stratification). These benefits include providing more precise estimates of the true treatment effects as well as saving time and money. This savings results from an ability to avoid recruitment of subjects who may not be appropriate for particular studies. The propensity score is not the only tool that can be used in analysis of data from observational studies; rather, it should be thought of as an additional tool available to investigators as they try to estimate the effects of treatments in studies where potential bias may exist.

## Acknowledgments

The author would like to thank his wife Carey and his family for their support.

**Source of Funding**

This work was supported in part by National Cancer Institute Grant 1 RO1 CA79934.

**Disclosures**

None.

## References

- ↵
- ↵
Bild DE, Bluemke DA, Burke GL, Detrano R, Diez-Roux AV, Folsom AR, Greenland P, Jacobs DR, Kronmal R, Liu K, Nelson JC, O’Leary D, Saad MF, Shea S, Szklo M, Tracy RP. The Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol
*.*2002; 156: 871–881. - ↵
- ↵
Sabatine MS, Morrow DA, Montalescot G, Dellborg M, Leiva-Pons JL, Keltai M, Murphy SA, McCabe CH, Gibson CM, Cannon CP, Antman EM, Braunwald E. Angiographic and clinical outcomes in patients receiving low-molecular-weight heparin versus unfractionated heparin in ST-elevation myocardial infarction treated with fibrinolytics in the CLARITY-TIMI 28 trial. Circulation
*.*2005; 112: 3846–3854. - ↵
- ↵
- ↵
- ↵
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika
*.*1983; 70: 41–55. - ↵
- ↵
Chertow GM, Normand SL, McNeil BJ. Renalism: inappropriately low rates of coronary angiography in elderly individuals with renal insufficiency. J Am Soc Nephrol
*.*2004; 15: 2462–2468. - ↵
- ↵

## This Issue

## Jump to

## Article Tools

- Propensity Scores in Cardiovascular ResearchRalph B. D’AgostinoCirculation. 2007;115:2340-2343, originally published April 30, 2007http://dx.doi.org/10.1161/CIRCULATIONAHA.105.594952
## Citation Manager Formats

## Share this Article

- Propensity Scores in Cardiovascular ResearchRalph B. D’AgostinoCirculation. 2007;115:2340-2343, originally published April 30, 2007http://dx.doi.org/10.1161/CIRCULATIONAHA.105.594952

## Related Articles

- No related articles found.

## Cited By...

- Zofenopril and ramipril in patients with left ventricular systolic dysfunction after acute myocardial infarction: A propensity analysis of the Survival of Myocardial Infarction Long-term Evaluation (SMILE) 4 study
- The association between the transfusion of small volumes of leucocyte-depleted red blood cells and outcomes in patients undergoing open-heart valve surgery
- Long-Term Outcomes of the Ross Procedure Versus Mechanical Aortic Valve Replacement: Propensity-Matched Cohort Study
- Best (but oft-forgotten) practices: propensity score methods in clinical nutrition research
- No difference between aspirin and warfarin after extracardiac Fontan in a propensity score analysis of 475 patients
- Should Chronic Total Occlusion Be Treated With Coronary Artery Bypass Grafting?: Chronic Total Occlusion Should Not Routinely Be Treated With Coronary Artery Bypass Grafting
- Cardiovascular Risk Factor Targets and Cardiovascular Disease Event Risk in Diabetes: A Pooling Project of the Atherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis, and Jackson Heart Study
- Excess short-term mortality in women after isolated coronary artery bypass graft surgery
- Transfusion of 1 and 2 units of red blood cells does not increase mortality and organ failure in patients undergoing isolated coronary artery bypass grafting
- Tricuspid valve repair in patients with left-ventricular assist device implants and tricuspid valve regurgitation: propensity score-adjusted analysis of clinical outcome
- Effect of Urate-lowering Therapy on the Risk of Cardiovascular Disease and All-cause Mortality in Patients with Gout: A Case-matched Cohort Study
- Statistical and data reporting guidelines for the European Journal of Cardio-Thoracic Surgery and the Interactive CardioVascular and Thoracic Surgery
- Echocardiographic Findings Predict In-Hospital and 1-Year Mortality in Left-Sided Native Valve Staphylococcus aureus Endocarditis: Analysis From the International Collaboration on Endocarditis-Prospective Echo Cohort Study
- Racial Differences in Heart Failure Outcomes: Evidence From the Tele-HF Trial (Telemonitoring to Improve Heart Failure Outcomes)
- Intermittent warm blood versus cold crystalloid cardioplegia for myocardial protection: a propensity score-matched analysis of 12-year single-center experience
- Serum bilirubin and the risk of hypertension
- What is a performance outlier?
- Cardiac rehabilitation is associated with reduced long-term mortality in patients undergoing combined heart valve and CABG surgery
- Association of Medical Treatment Nonadherence With All-Cause Mortality in Newly Treated Hypertensive US Veterans
- Obstructive sleep apnoea treatment and fasting lipids: a comparative effectiveness study
- Surgical Revascularization Is Associated With Maximal Survival in Patients With Ischemic Mitral Regurgitation: A 20-Year Experience
- Warfarin Use and the Risk for Stroke and Bleeding in Patients With Atrial Fibrillation Undergoing Dialysis
- Predialysis Health, Dialysis Timing, and Outcomes among Older United States Adults
- Is the Effect of Compulsory Community Treatment on Preventable Deaths from Physical Disorders Mediated by Better Access to Specialized Medical Procedures?
- An Introduction to Propensity Scores: What, When, and How
- Video-assisted thoracic surgery versus open thoracotomy for non-small-cell lung cancer: a propensity score analysis based on a multi-institutional registry
- Clinical response with adaptive CRT algorithm compared with CRT with echocardiography-optimized atrioventricular delay: a retrospective analysis of multicentre trials
- Postthrombolysis Outcomes in Acute Ischemic Stroke Patients of Asian Race-Ethnicity
- Participation in Cardiac Rehabilitation and Survival After Coronary Artery Bypass Graft Surgery: A Community-Based Study
- Lack of evidence of increased mortality among patients with atrial fibrillation taking digoxin: findings from post hoc propensity-matched analysis of the AFFIRM trial
- Video-assisted thoracic surgery versus open thoracotomy for non-small cell lung cancer: a meta-analysis of propensity score-matched patients
- Association Between Conformity With Performance Measures and 1-Year Postdischarge Survival in Patients With Acute Decompensated Heart Failure
- Beta and Angiotensin Blockades Are Associated With Improved 10-Year Survival in Renal Transplant Recipients
- The Extent that Noncompliance with the Ten Steps to Successful Breastfeeding Influences Breastfeeding Duration
- Reducing all-cause mortality among patients with psychiatric disorders: a population-based study
- Moderate dosage of tranexamic acid during cardiac surgery with cardiopulmonary bypass and convulsive seizures: incidence and clinical outcome
- Insulin Resistance and the Risk of Stroke and Stroke Subtypes in the Nondiabetic Elderly
- A Pilot Study Examining the Severity and Outcome of the Post-Cardiac Arrest Syndrome: A Comparative Analysis of Two Geographically Distinct Hospitals
- Successful Recanalization of Chronic Total Occlusions Is Associated With Improved Long-Term Survival
- Early Detection of an Underperforming Implantable Cardiovascular Device Using an Automated Safety Surveillance Tool
- Long-Term Comparison of Everolimus- and Sirolimus-Eluting Stents in Patients With Acute Coronary Syndromes
- Transradial Versus Transfemoral Intervention for Acute Myocardial Infarction: A Propensity Score-Adjusted and -Matched Analysis From the REAL (REgistro regionale AngiopLastiche dell'Emilia-Romagna) Multicenter Registry
- Serum Lipid Levels and the Risk of Intracerebral Hemorrhage: The Rotterdam Study
- The Impact of Renin-Angiotensin-Aldosterone System Blockade on Heart Failure Outcomes and Mortality in Patients Identified to Have Aortic Regurgitation: A Large Population Cohort Study
- Long-Term Prevention of Stroke: A Modern Comparison of Current Carotid Stenting and Carotid Endarterectomy
- Venesection for non-alcoholic fatty liver disease unresponsive to lifestyle counselling--a propensity score-adjusted observational study
- Long-Term Outcomes of Endoscopic Vein Harvesting After Coronary Artery Bypass Grafting
- Increased Pericardial Fat Volume Measured From Noncontrast CT Predicts Myocardial Ischemia by SPECT
- Prognostic Implications of Mitral Regurgitation in Patients With Severe Aortic Regurgitation
- Impact of the Extent of Coronary Artery Disease on Outcomes After Revascularization for Unprotected Left Main Coronary Artery Stenosis
- Quantifying the Early Health Status Benefits of Successful Chronic Total Occlusion Recanalization: Results From the FlowCardia's Approach to Chronic Total Occlusion Recanalization (FACTOR) Trial
- Medical Treatment Predicts Mortality After Hip Fracture
- Pericardial Fat Burden on ECG-Gated Noncontrast CT in Asymptomatic Patients Who Subsequently Experience Adverse Cardiovascular Events
- Increased Mortality Associated With Low Use of Clopidogrel in Patients With Heart Failure and Acute Myocardial Infarction Not Undergoing Percutaneous Coronary Intervention: A Nationwide Study
- Surgical Timing in Infectious Endocarditis: Wrestling With the Unrandomized
- Hypokalemia and Outcomes in Patients With Chronic Heart Failure and Chronic Kidney Disease: Findings From Propensity-Matched Studies
- Aprotinin in cardiac surgery patients: is the risk worth the benefit?
- Adherence to Antihypertensive Medications and Cardiovascular Morbidity Among Newly Diagnosed Hypertensive Patients
- Determinants and outcomes of coronary angiography after non-ST-segment elevation myocardial infarction. A cohort study of the Myocardial Ischaemia National Audit Project (MINAP)
- Effect of Beta-Blocker Therapy on Survival in Patients With Severe Aortic Regurgitation: Results From a Cohort of 756 Patients
- Impact of Intravascular Ultrasound Guidance on Long-Term Mortality in Stenting for Unprotected Left Main Coronary Artery Stenosis
- The Risk of Stent Thrombosis in Patients With Acute Coronary Syndromes Treated With Bare-Metal and Drug-Eluting Stents
- Review of A Large Clinical Series: Coronary Angiography Predicts Improved Outcome Following Cardiac Arrest: Propensity-adjusted Analysis
- Characteristics and Outcomes of Revascularized Patients With Hypertension: An International Verapamil SR-Trandolapril Substudy
- Temporal management patterns and outcomes of non-ST elevation acute coronary syndromes in patients with kidney dysfunction
- Impact of Age and Medical Comorbidity on the Effectiveness of Implantable Cardioverter-Defibrillators for Primary Prevention
- Continuation or Withdrawal of Beta-Blocker Therapy in Patients Admitted for Heart Failure
- Letter by Barlis et al Regarding Article, "Two-Year Clinical Outcomes With Drug-Eluting Stents for Diabetic Patients With De Novo Coronary Lesions: Results From a Real-World Multicenter Registry
- Response to Letters Regarding Article, "Two-Year Clinical Outcomes With Drug-Eluting Stents for Diabetic Patients With De Novo Coronary Lesions: Results From a Real-World Multicenter Registry"
- Adiponectin and prognostic outcome in patients with coronary artery disease
- Observational approach to subjects with mild-to-moderate head injury and initial non-neurosurgical lesions
- The Use of Self-Generated Identification Codes in Longitudinal Research
- Primary Percutaneous Coronary Intervention for Acute Myocardial Infarction: Long-Term Outcome After Bare Metal and Drug-Eluting Stent Implantation
- Contemporary Analysis of Descending Thoracic and Thoracoabdominal Aneurysm Repair: A Comparison of Endovascular and Open Techniques
- Percutaneous Transcatheter Aortic Valve Implantation: Assessing Results, Judging Outcomes, and Planning Trials: The Interventionalist Perspective
- Comparison of Coronary Artery Bypass Surgery and Percutaneous Drug-Eluting Stent Implantation for Treatment of Left Main Coronary Artery Stenosis
- Vascular Remodeling and Duration of Hypertension Predict Outcome of Adrenalectomy in Primary Aldosteronism Patients
- Studies of Drug-Eluting Stents: To Each His Own?
- Comparison of "Risk-Adjusted" Hospital Outcomes

This article has not yet been cited by articles in journals that are participating in Crossref Cited-by Linking.