Appendix 1.
The following investigators, coordinators, and centers participated in the study: P. Desai, M.
Betzen, D. DeLuna, Amarillo Heart Clinical Research Institute, Amarillo; J. Whitehill, J. Hatch,
L. Janak, R. Cherry, Austin Heart P.A., Austin; E. Gonzalez, I. Cruz, Baptist Cardiac & Vascular
Institute, Miami; E. Johnson, C. Allbritton, V. Derrick, Baptist Memorial Hospital, Memphis; R.
Fishman, V. Assalone, L. Mahon, Bridgeport Hospital, Bridgeport; D. Lustgarten, M. Rowen,
M. Bessette, B. Alemy, Fletcher Allen Health Care, Burlington; D. Dan, K. Picardi, Fuqua Heart
Center, Atlanta; C. Schuger, J. Dzidowski, M. McCarthy, P. Fields, S. Alexander, Henry Ford
Hospital, Detroit; G. Nair, R. Kovacs, D. Beasley, T. Strickland, J. Marks, IU Health Methodist
Research Institute, Indianapolis; S. Beau, J. Tableriou, B. Griffin, Little Rock Cardiology Clinic,
Little Rock; J. Shani, M. Stokes-McCarthy, G. Tan-Augenstein, G. Pace, H. Brosnan,
Maimonides Medical Center, New York; J. Hayes, K. Mancl, K. Maassen, Marshfield Clinic,
Marshfield; D. Fintel, L. Karpf, T. Abraham, K. Campione, E. Martin, Northwestern Memorial
Hospital, Chicago; D. Bello, I. Viera Fleetwood, M. Tinetti, R. Rock, Orlando Regional Medical
Center, Orlando; J. Simonson, S. Barnes, J. Letexier, M. Strothman, J. Mattson, Park Nicollet
Institute, Minneapolis; C. Albert Shoultz III, S. Ali, Providence Healthcare Network; D. Abbott,
M. Medeiros, J. McKeon, Rhode Island Hospital/Brown Medical School, Providence; A. Nichols,
T. Edwards, C. Watts, C. Alley, Riverside Methodist Hospital, Columbus; M. Romanelli, D.
Steffen, R. Henschel, S. Teller, L. Froehlich, R. Bess, St. John Hospital & Medical Center,
Detroit; W. Warnica, B. Smith, D. Eichman, D. Scarcelli, University of Calgary, Calgary; N.
Badhwar, P. Malone, D. Green, S. Iyer, University of California at San Francisco, San
Francisco; R. Germany, C. Murray, G. Straughn, K. Drennan, University of Oklahoma,
Oklahoma City; O. Marroquin, L. Dennis, C. Farrow, L. Baxendell, S. Grate, M. Enlow,
University of Pittsburgh Medical Center, Pittsburgh; W. Zareba, I. Chaudhary, P. Laduke, V.
Conary-Rocco, L. Caufield, C. Patterson, University of Rochester - Strong Memorial Hospital,
Rochester; A. Warner, J. Johnson, West Los Angeles VA Medical Center, Los Angeles; J.
Germano, W. Drewes, B. George, B. Yoo, D. Patel, Winthrop University Hospital, Mineola.
Steering Committee - R. Bonow*, Northwestern University, Chicago; M. Cuffe*, Hospital
Corporation of America; A. Dyer, Northwestern University, Chicago; P. Greenland,
Northwestern University, Chicago J. Goldberger*, Northwestern University, Chicago; R.
O’Rourke, University of Texas Health Science Center at San Antonio, San Antonio; Y.
Rosenberg*, National Heart, Lung, and Blood Institute, Bethesda; P. Shah*, Cedar-Sinai
Medical Center, Los Angeles; S. Smith*, University of North Carolina, Chapel Hill.
* current members (all original members are listed)
Observational Study Monitoring Board- R. Byington, Wake Forest University Health
Science, Winston-Salem, N.C.; Z. Feng, Fred Hutchinson Cancer Research Center, Seattle; S.
Goldstein, Henry Ford Hospital, Detroit; J. Kirkpatrick, Hospital of the University of
Pennsylvania; C. Love, New York University Medical Center, New York; S. Singh, Veteran
Affairs Medical Center, Washington, D.C.
National Heart, Lung, and Blood Institute - Y. Rosenberg, S. Goldberg, M. Kwak, A. Rao, P.
Srinivas
Northwestern University Clinical and Data Coordinating Center- J. Goldberger, C. Ball,
J. Cahill, A. Schaechter, D. Alexander, K. Ma, T. Plant, A. Rosenfeld, J. Scofic, J. Simon, H.
Subačius
APPENDIX 2
This Appendix provides detail on the implementation of propensity score adjustment. First,
we show the SAS code used to calculate the propensity score. Second, we present the
parameters obtained in the propensity score model. Third, we show the histogram of the
propensity scores for each treatment arm.
We used the propensity score as a control variable (including quadratic and cubic
polynomials) in a proportional hazards frailty regression model identical to all other
survival analyses in this manuscript. There are two advantages of this approach over
matching. First, no patients are excluded from the sample on account of non-overlapping
propensity scores among the four groups, providing a larger sample size. Second, it is
challenging in practice to do propensity score matching when there are more than 2
treatment arms, in particular 4 groups for this analysis.
1. SAS Code for calculation of the propensity score
%let Class = Male White Hispanic SmokeCurrent
Diabetes Hypertension HyperChol STEMI Lytic_IH PPCI_IH
PrevMI CABG ReVasc COPD ESRD CHFHx CHFDx CVATIA Anterior ICDall
ASA ACEIARB Statin Clopidogrel MetoprololDC CarvedilolDC;
%let Cubic = Age|Age|Age lnTroponin|lnTroponin|lnTroponin LVEF|LVEF|LVEF
lnLOS|lnLOS|lnLOS BMI|BMI|BMI RestSBP|RestSBP|RestSBP PRestHR|PRestHR|PRestHR;
/* Calculate the Propensity Score--Risk-Adjusted Dose
*/
proc GLIMMIX data=OutMi;
by _Imputation_;
class SiteNumber;
model BBDosePctN = &Class &Cubic
random SiteNumber;
run;
Note that PS calculation and PH regression were both done on 5 sets of multiply imputed
data; hence, the “by_Imputation_;” line in the SAS code.
2. SAS output of the propensity score model
Effect
Estimate Standard Error DF
t Value p-value
Intercept
73.3402 27.3697
25
2.68
0.0128
Male
1.5484
5972 2.08
0.0373
White
-2.4658 0.8674
5972 -2.84
0.0045
Hispanic
-2.651
1.3689
5972 -1.94
0.0528
Current smoker
-1.4012 0.7751
5972 -1.81
0.0707
Diabetes
0.5209
0.7763
5972 0.67
0.5022
Hypertension
5.8958
0.7846
5972 7.51
<0.0001
Hyperlipidemia
1.9786
0.7125
5972 2.78
0.0055
STEMI
-0.9833 0.9018
5972 -1.09
0.2756
Anterior location
1.0747
5972 0.99
0.3206
Thrombolytic therapy
-2.0679 1.3413
5972 -1.54
0.1232
Primary PCI
-4.2603 0.91
5972 -4.68
<0.0001
Previous MI
2.0496
0.8681
5972 2.36
0.0183
History of CABG
7.1149
1.0619
5972 6.7
<0.0001
In-hospital revascularization -3.0209 0.9034
5972 -3.34
0.0008
COPD
-2.1153 1.1341
5972 -1.87
0.0622
ESRD
2.0759
1.8872
5972 1.1
0.2714
CHF History
4.1411
1.2163
5972 3.4
0.0007
Heart failure on admission
-1.0349 1.2118
5972 -0.85
0.3931
CVA/TIA
2.145
1.108
5972 1.94
0.0529
ICD
4.3632
1.9198
5972 2.27
0.0231
ASA
0.02195 1.3538
5972 0.02
0.9871
ACE-I/ARB
1.5641
0.738
5972 2.12
0.0341
Statin
0.9858
1.1329
5972 0.87
0.3842
Clopidogrel
1.7464
0.8085
5972 2.16
0.0308
Metoprolol
-11.7374 1.3423
5972 -8.74
<0.0001
Carvedilol
-13.5138 1.5081
5972 -8.96
<0.0001
Age linear
-1.0941 0.9973
5972 -1.1
0.2727
Age quadratic
0.0186
5972 1.14
0.253
0.7435
1.0818
0.01627
Age cubic
-0.00011 0.000086
5972 -1.27
0.2032
ln(Troponin)
0.09247 0.357
5972 0.26
0.7956
ln(Troponin) quadratic
0.1224
5972 0.74
0.4622
ln(Troponin) cubic
-0.02285 0.02931
5972 -0.78
0.4357
LVEF linear
0.06153 0.4686
5972 0.13
0.8955
LVEF quadratic
0.003227 0.01111
5972 0.29
0.7715
LVEF cubic
-0.00004 0.000083
5972 -0.46
0.6448
ln(LOS) linear
3.441
6.3559
5972 0.54
0.5883
ln(LOS) quadratic
2.438
3.0462
5972 0.8
0.4235
ln(LOS) cubic
-0.7217 0.4552
5972 -1.59
0.1129
BMI linear
-1.5172 0.8868
5972 -1.71
0.0872
BMI quadratic
0.05293 0.02482
5972 2.13
0.033
BMI cubic
-0.00048 0.00022
5972 -2.18
0.0296
Admission SBP linear
-0.5449 0.2697
5972 -2.02
0.0434
Admission SBP quadratic
0.00387 0.00183
5972 2.11
0.0345
Admission SBP cubic
-7.17E-06 4.04E-06
5972 -1.78
0.0757
Admission HR linear
0.07088 0.2491
5972 0.28
0.776
Admission HR quadratic
0.00052 0.002421
5972 0.21
0.8298
Admission HR cubic
-1.91E-06 7.39E-06
5972 -0.26
0.796
0.1664
Abbreviations as in Table 1: ASA-aspirin, ACE-I-angiotensin converting enzyme inhibitor,
ARB-angiotensin receptor blocker, BMI-body mass index, CABG-coronary artery bypass
graft surgery, CHF- congestive heart failure, COPD-chronic obstructive pulmonary disease,
CVA-cerebrovascular accident, ESRD-end stage renal disease, HR-heart rate, ICDimplantable cardioverter defibrillator, LOS-length of stay, LVEF-left ventricular ejection
fraction, MI-myocardial infarction, PCI-percutaneous coronary intervention, SBP-systolic
blood pressure, STEMI-ST elevation MI, TIA-transient ischemic attack
Histogram of the propensity scores by discharge beta-blocker category.
It can be seen that the propensity scores of all four dose categories overlap for a substantial
portion of the distribution, noting that the dose category >50% has more propensity scores
with higher values. The propensity score adjustment can reduce selection bias caused by
such imbalance in covariate values.