[Skip to Content]
[Skip to Content Landing]
Figure 1.
Kaplan-Meier Estimated Mortality, Overall and by Presenting Symptoms
Kaplan-Meier Estimated Mortality, Overall and by Presenting Symptoms

CAS indicates carotid artery stenting; CEA, carotid endarterectomy.

Figure 2.
Hazard Ratios (HRs) of Mortality for Carotid Endarterectomy vs Carotid Stenting
Hazard Ratios (HRs) of Mortality for Carotid Endarterectomy vs Carotid Stenting

The CREST trial outcome represented is long-term stroke or periprocedural myocardial infarction, stroke, or death. ACT 1 indicates Asymptomatic Carotid Trial; CREST, Carotid Revascularization Endarterectomy vs Stenting Trial; RCT, randomized clinical trial; VQI, Vascular Quality Initiative.

Table 1.  
Cohort Characteristics
Cohort Characteristics
Table 2.  
Mortality HRs for Carotid Endarterectomy vs Carotid Stenting
Mortality HRs for Carotid Endarterectomy vs Carotid Stenting
1.
Barnett  HJM, Taylor  DW, Haynes  RB,  et al; North American Symptomatic Carotid Endarterectomy Trial Collaborators.  Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis.  N Engl J Med. 1991;325(7):445-453. doi:10.1056/NEJM199108153250701PubMedGoogle ScholarCrossref
2.
 Endarterectomy for asymptomatic carotid artery stenosis: executive committee for the Asymptomatic Carotid Atherosclerosis Study.  JAMA. 1995;273(18):1421-1428. doi:10.1001/jama.1995.03520420037035PubMedGoogle ScholarCrossref
3.
Halliday  A, Mansfield  A, Marro  J,  et al; MRC Asymptomatic Carotid Surgery Trial Collaborative Group.  Prevention of disabling and fatal strokes by successful carotid endarterectomy in patients without recent neurological symptoms: randomised controlled trial.  Lancet. 2004;363(9420):1491-1502. doi:10.1016/S0140-6736(04)16146-1PubMedGoogle ScholarCrossref
4.
Brott  TG, Halperin  JL, Abbara  S,  et al; American College of Cardiology Foundation; American Stroke Association; American Association of Neurological Surgeons; American College of Radiology; American Society of Neuroradiology; Congress of Neurological Surgeons; Society of Atherosclerosis Imaging and Prevention; Society for Cardiovascular Angiography and Interventions; Society of Interventional Radiology; Society of NeuroInterventional Surgery; Society for Vascular Medicine; Society for Vascular Surgery.  2011 ASA/ACCF/AHA/AANN/AANS/ACR/ASNR/CNS/SAIP/SCAI/SIR/SNIS/SVM/SVS guideline on the management of patients with extracranial carotid and vertebral artery disease: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American Stroke Association, American Association of Neuroscience Nurses, American Association of Neurological Surgeons, American College of Radiology, American Society of Neuroradiology, Congress of Neurological Surgeons, Society of Atherosclerosis Imaging and Prevention, Society for Cardiovascular Angiography and Interventions, Society of Interventional Radiology, Society of NeuroInterventional Surgery, Society for Vascular Medicine, and Society for Vascular Surgery.  Circulation. 2011;124(4):489-532. doi:10.1161/CIR.0b013e31820d8d78PubMedGoogle ScholarCrossref
5.
Manson  JE, Stampfer  MJ, Colditz  GA,  et al.  A prospective study of aspirin use and primary prevention of cardiovascular disease in women.  JAMA. 1991;266(4):521-527. doi:10.1001/jama.1991.03470040085027PubMedGoogle ScholarCrossref
6.
Kannel  WB, Larson  M.  Long-term epidemiologic prediction of coronary disease: the Framingham experience.  Cardiology. 1993;82(2-3):137-152. doi:10.1159/000175864PubMedGoogle ScholarCrossref
7.
Baigent  C, Blackwell  L, Collins  R,  et al; Antithrombotic Trialists’ Collaboration.  Aspirin in the primary and secondary prevention of vascular disease: collaborative meta-analysis of individual participant data from randomised trials.  Lancet. 2009;373(9678):1849-1860. doi:10.1016/S0140-6736(09)60503-1PubMedGoogle ScholarCrossref
8.
US Preventive Services Task Force.  Aspirin for the prevention of cardiovascular disease: U.S. Preventive Services Task Force recommendation statement.  Ann Intern Med. 2009;150(6):396-404. doi:10.7326/0003-4819-150-6-200903170-00008PubMedGoogle ScholarCrossref
9.
Lederle  FA, Freischlag  JA, Kyriakides  TC,  et al; Open Versus Endovascular Repair Veterans Affairs Cooperative Study Group.  Outcomes following endovascular vs open repair of abdominal aortic aneurysm: a randomized trial.  JAMA. 2009;302(14):1535-1542. doi:10.1001/jama.2009.1426PubMedGoogle ScholarCrossref
10.
De Bruin  JL, Baas  AF, Buth  J,  et al; DREAM Study Group.  Long-term outcome of open or endovascular repair of abdominal aortic aneurysm.  N Engl J Med. 2010;362(20):1881-1889. doi:10.1056/NEJMoa0909499PubMedGoogle ScholarCrossref
11.
Schermerhorn  ML, Buck  DB, O’Malley  AJ,  et al.  Long-Term outcomes of abdominal aortic aneurysm in the medicare population.  N Engl J Med. 2015;373(4):328-338. doi:10.1056/NEJMoa1405778PubMedGoogle ScholarCrossref
12.
Giles  KA, Hamdan  AD, Pomposelli  FB, Wyers  MC, Schermerhorn  ML.  Stroke and death after carotid endarterectomy and carotid artery stenting with and without high risk criteria.  J Vasc Surg. 2010;52(6):1497-1504. doi:10.1016/j.jvs.2010.06.174PubMedGoogle ScholarCrossref
13.
McPhee  JT, Schanzer  A, Messina  LM, Eslami  MH.  Carotid artery stenting has increased rates of postprocedure stroke, death, and resource utilization than does carotid endarterectomy in the United States, 2005.  J Vasc Surg. 2008;48(6):1442-1450, 1450e1. doi:10.1016/j.jvs.2008.07.017PubMedGoogle ScholarCrossref
14.
Wang  FW, Esterbrooks  D, Kuo  YF, Mooss  A, Mohiuddin  SM, Uretsky  BF.  Outcomes after carotid artery stenting and endarterectomy in the Medicare population.  Stroke. 2011;42(7):2019-2025. doi:10.1161/STROKEAHA.110.608992PubMedGoogle ScholarCrossref
15.
Nolan  BW, De Martino  RR, Goodney  PP,  et al; Vascular Study Group of New England.  Comparison of carotid endarterectomy and stenting in real world practice using a regional quality improvement registry.  J Vasc Surg. 2012;56(4):990-996. doi:10.1016/j.jvs.2012.03.009PubMedGoogle ScholarCrossref
16.
Rosenfield  K, Matsumura  JS, Chaturvedi  S,  et al; ACT I Investigators.  Randomized trial of stent versus surgery for asymptomatic carotid stenosis.  N Engl J Med. 2016;374(11):1011-1020. doi:10.1056/NEJMoa1515706PubMedGoogle ScholarCrossref
17.
Bonati  LH, Dobson  J, Featherstone  RL,  et al; International Carotid Stenting Study Investigators.  Long-term outcomes after stenting versus endarterectomy for treatment of symptomatic carotid stenosis: the International Carotid Stenting Study (ICSS) randomised trial.  Lancet. 2015;385(9967):529-538. doi:10.1016/S0140-6736(14)61184-3PubMedGoogle ScholarCrossref
18.
Anglemyer  A, Horvath  HT, Bero  L.  Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials.  Cochrane Database Syst Rev. 2014;(4):MR000034.PubMedGoogle Scholar
19.
Rothwell  PM.  External validity of randomised controlled trials: “to whom do the results of this trial apply?”  Lancet. 2005;365(9453):82-93. doi:10.1016/S0140-6736(04)17670-8PubMedGoogle ScholarCrossref
20.
Dhruva  SS, Redberg  RF.  Variations between clinical trial participants and Medicare beneficiaries in evidence used for Medicare national coverage decisions.  Arch Intern Med. 2008;168(2):136-140. doi:10.1001/archinternmed.2007.56PubMedGoogle ScholarCrossref
21.
Grimes  DA, Schulz  KF.  Bias and causal associations in observational research.  Lancet. 2002;359(9302):248-252. doi:10.1016/S0140-6736(02)07451-2PubMedGoogle ScholarCrossref
22.
Stukel  TA, Fisher  ES, Wennberg  DE, Alter  DA, Gottlieb  DJ, Vermeulen  MJ.  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.  JAMA. 2007;297(3):278-285. doi:10.1001/jama.297.3.278PubMedGoogle Scholar
23.
D’Agostino  RB  Jr.  Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.  Stat Med. 1998;17(19):2265-2281. doi:10.1002/(SICI)1097-0258(19981015)17:19<2265::AID-SIM918>3.0.CO;2-BPubMedGoogle Scholar
24.
Cox  DR.  Regression models and life-tables. J R Stat Soc B. 1972;34(2):187-220. http://www.jstor.org/stable/2985181. Accessed September 1, 2017.
25.
De Martino  RR, Brooke  BS, Robinson  W,  et al.  Designation as “unfit for open repair” is associated with poor outcomes after endovascular aortic aneurysm repair.  Circ Cardiovasc Qual Outcomes. 2013;6(5):575-581. doi:10.1161/CIRCOUTCOMES.113.000303PubMedGoogle Scholar
26.
Hernán  MA, Robins  JM.  Instruments for causal inference: an epidemiologist’s dream?  Epidemiology. 2006;17(4):360-372. doi:10.1097/01.ede.0000222409.00878.37PubMedGoogle Scholar
27.
Terza  JV, Basu  A, Rathouz  PJ.  Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling.  J Health Econ. 2008;27(3):531-543. doi:10.1016/j.jhealeco.2007.09.009PubMedGoogle Scholar
28.
MacKenzie  TA, Tosteson  TD, Morden  NE, Stukel  TA, O’Malley  AJ.  Using instrumental variables to estimate a Cox’s proportional hazards regression subject to additive confounding.  Health Serv Outcomes Res Methodol. 2014;14(1-2):54-68. doi:10.1007/s10742-014-0117-xPubMedGoogle Scholar
29.
Aalen  OO, Cook  RJ, Røysland  K.  Does Cox analysis of a randomized survival study yield a causal treatment effect?  Lifetime Data Anal. 2015;21(4):579-593. doi:10.1007/s10985-015-9335-yPubMedGoogle Scholar
30.
Li  J, Fine  J, Brookhart  A.  Instrumental variable additive hazards models.  Biometrics. 2015;71(1):122-130. doi:10.1111/biom.12244PubMedGoogle Scholar
31.
Martinussen  T, Nørbo Sørensen  D, Vansteelandt  S.  Instrumental variables estimation under a structural Cox model.  Biostatistics. 2017.PubMedGoogle Scholar
32.
Martínez-Camblor  P, Mackenzie  T, Staiger  DO, Goodney  PP, O’Malley  AJ.  Adjusting for bias introduced by instrumental variable estimation in the Cox proportional hazards model.  Biostatistics. 2017. doi:10.1093/biostatistics/kxx062PubMedGoogle Scholar
33.
Vascular Quality Initiative. http://www.vascularqualityinitiative.org/. Accessed March 1, 2017.
34.
Centers for Medicare and Medicaid Services. http://www.cms.gov. Accessed March 17, 2017.
35.
Hoel  AW, Faerber  AE, Moore  KO,  et al.  A pilot study for long-term outcome assessment after aortic aneurysm repair using vascular quality initiative data matched to Medicare claims.  J Vasc Surg. 2017;66(3):751-759.e1. doi:10.1016/j.jvs.2016.12.100PubMedGoogle Scholar
36.
Vandenbroucke  JP, von Elm  E, Altman  DG,  et al; STROBE Initiative.  Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.  PLoS Med. 2007;4(10):e297. doi:10.1371/journal.pmed.0040297PubMedGoogle Scholar
37.
Skerritt  MR, Block  RC, Pearson  TA, Young  KC.  Carotid endarterectomy and carotid artery stenting utilization trends over time.  BMC Neurol. 2012;12:17. doi:10.1186/1471-2377-12-17PubMedGoogle Scholar
38.
Cortese  G, Scheike  TH, Martinussen  T.  Flexible survival regression modelling.  Stat Methods Med Res. 2010;19(1):5-28. doi:10.1177/0962280209105022PubMedGoogle Scholar
39.
Rosenbaum  PR, Rubin  DB.  Constructing a control group using multivariate matched sampling methods that incorporate the propensity score.  Am Stat. 1985;39(1):33-38. doi:10.2307/2683903Google Scholar
40.
Stock  J, Yogo  M. Testing for weak instruments in linear IV regression. In: Andrews  DWK, ed.  Identification and Inference for Econometric Models. New York, NY: Cambridge University Press; 2005:80-108. doi:10.1017/CBO9780511614491.006
41.
West  SG, Duan  N, Pequegnat  W,  et al.  Alternatives to the randomized controlled trial.  Am J Public Health. 2008;98(8):1359-1366. doi:10.2105/AJPH.2007.124446PubMedGoogle Scholar
42.
Staiger  D, Stock  JH.  Instrumental variables regression with weak instruments.  Econometrica. 1997;65(3):557-586. doi:10.2307/2171753Google Scholar
43.
Rassen  JA, Schneeweiss  S, Glynn  RJ, Mittleman  MA, Brookhart  MA.  Instrumental variable analysis for estimation of treatment effects with dichotomous outcomes.  Am J Epidemiol. 2009;169(3):273-284. doi:10.1093/aje/kwn299PubMedGoogle Scholar
44.
Wan  F, Small  D, Bekelman  JE, Mitra  N.  Bias in estimating the causal hazard ratio when using two-stage instrumental variable methods.  Stat Med. 2015;34(14):2235-2265. doi:10.1002/sim.6470PubMedGoogle Scholar
45.
Cai  B, Small  DS, Have  TR.  Two-stage instrumental variable methods for estimating the causal odds ratio: analysis of bias.  Stat Med. 2011;30(15):1809-1824. doi:10.1002/sim.4241PubMedGoogle Scholar
46.
MacKenzie  TA, Brown  JR, Likosky  DS, Wu  Y, Grunkemeier  GL.  Review of case-mix corrected survival curves.  Ann Thorac Surg. 2012;93(5):1416-1425. doi:10.1016/j.athoracsur.2011.12.094PubMedGoogle Scholar
47.
Tan  HJ, Norton  EC, Ye  Z, Hafez  KS, Gore  JL, Miller  DC.  Long-term survival following partial vs radical nephrectomy among older patients with early-stage kidney cancer.  JAMA. 2012;307(15):1629-1635. doi:10.1001/jama.2012.475PubMedGoogle Scholar
48.
Brott  TG, Howard  G, Roubin  GS,  et al; CREST Investigators.  Long-term results of stenting versus endarterectomy for carotid-artery stenosis.  N Engl J Med. 2016;374(11):1021-1031. doi:10.1056/NEJMoa1505215PubMedGoogle Scholar
49.
Paraskevas  KI, Kalmykov  EL, Naylor  AR.  Stroke/death rates following carotid artery stenting and carotid endarterectomy in contemporary administrative dataset registries: a systematic review.  Eur J Vasc Endovasc Surg. 2016;51(1):3-12. doi:10.1016/j.ejvs.2015.07.032PubMedGoogle Scholar
50.
Timaran  CH, Veith  FJ, Rosero  EB, Modrall  JG, Valentine  RJ, Clagett  GP.  Intracranial hemorrhage after carotid endarterectomy and carotid stenting in the United States in 2005.  J Vasc Surg. 2009;49(3):623-628. doi:10.1016/j.jvs.2008.09.064PubMedGoogle Scholar
51.
US Department of Health and Human Services, US Food and Drug Administration.  Use of real-world evidence to support regulatory decision-making for Medical devices. https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm513027.pdf. Accessed September 1, 2017.
52.
Menkes  MS, Comstock  GW, Vuilleumier  JP, Helsing  KJ, Rider  AA, Brookmeyer  R.  Serum beta-carotene, vitamins A and E, selenium, and the risk of lung cancer.  N Engl J Med. 1986;315(20):1250-1254. doi:10.1056/NEJM198611133152003PubMedGoogle Scholar
53.
Stampfer  MJ, Colditz  GA.  Estrogen replacement therapy and coronary heart disease: a quantitative assessment of the epidemiologic evidence.  Prev Med. 1991;20(1):47-63. doi:10.1016/0091-7435(91)90006-PPubMedGoogle Scholar
54.
Alpha-Tocopherol, Beta Carotene Cancer Prevention Study Group.  The effect of vitamin E and beta carotene on the incidence of lung cancer and other cancers in male smokers.  N Engl J Med. 1994;330(15):1029-1035. doi:10.1056/NEJM199404143301501PubMedGoogle Scholar
55.
Khaw  KT, Bingham  S, Welch  A,  et al.  Relation between plasma ascorbic acid and mortality in men and women in EPIC-Norfolk prospective study: a prospective population study. European prospective investigation into cancer and nutrition.  Lancet. 2001;357(9257):657-663. doi:10.1016/S0140-6736(00)04128-3PubMedGoogle Scholar
56.
Beral  V, Banks  E, Reeves  G.  Evidence from randomised trials on the long-term effects of hormone replacement therapy.  Lancet. 2002;360(9337):942-944. doi:10.1016/S0140-6736(02)11032-4PubMedGoogle Scholar
57.
Heart Protection Study Collaborative Group.  MRC/BHF Heart Protection Study of antioxidant vitamin supplementation in 20,536 high-risk individuals: a randomised placebo-controlled trial.  Lancet. 2002;360(9326):23-33. doi:10.1016/S0140-6736(02)09328-5PubMedGoogle Scholar
58.
Deeks  JJ, Dinnes  J, D’Amico  R,  et al; International Stroke Trial Collaborative Group; European Carotid Surgery Trial Collaborative Group.  Evaluating non-randomised intervention studies.  Health Technol Assess. 2003;7(27):iii-x, 1-173. doi:10.3310/hta7270PubMedGoogle Scholar
59.
Kunz  R, Oxman  AD.  The unpredictability paradox: review of empirical comparisons of randomised and non-randomised clinical trials.  BMJ. 1998;317(7167):1185-1190. doi:10.1136/bmj.317.7167.1185PubMedGoogle Scholar
60.
Ioannidis  JP, Haidich  AB, Pappa  M,  et al.  Comparison of evidence of treatment effects in randomized and nonrandomized studies.  JAMA. 2001;286(7):821-830. doi:10.1001/jama.286.7.821PubMedGoogle Scholar
61.
Brewer  T, Colditz  GA.  Postmarketing surveillance and adverse drug reactions: current perspectives and future needs.  JAMA. 1999;281(9):824-829. doi:10.1001/jama.281.9.824PubMedGoogle Scholar
62.
Ross  SD.  Drug-related adverse events: a readers’ guide to assessing literature reviews and meta-analyses.  Arch Intern Med. 2001;161(8):1041-1046. doi:10.1001/archinte.161.8.1041PubMedGoogle Scholar
63.
Sutton  AJ, Cooper  NJ, Lambert  PC, Jones  DR, Abrams  KR, Sweeting  MJ.  Meta-analysis of rare and adverse event data.  Expert Rev Pharmacoecon Outcomes Res. 2002;2(4):367-379. doi:10.1586/14737167.2.4.367PubMedGoogle Scholar
64.
Ioannidis  JP, Mulrow  CD, Goodman  SN.  Adverse events: the more you search, the more you find.  Ann Intern Med. 2006;144(4):298-300. doi:10.7326/0003-4819-144-4-200602210-00013PubMedGoogle Scholar
65.
Newcombe  RG.  Explanatory and pragmatic estimates of the treatment effect when deviations from allocated treatment occur.  Stat Med. 1988;7(11):1179-1186. doi:10.1002/sim.4780071111PubMedGoogle Scholar
66.
Hearst  N, Newman  TB, Hulley  SB.  Delayed effects of the military draft on mortality: a randomized natural experiment.  N Engl J Med. 1986;314(10):620-624. doi:10.1056/NEJM198603063141005PubMedGoogle Scholar
67.
Brooke  BS, Goodney  PP, Kraiss  LW, Gottlieb  DJ, Samore  MH, Finlayson  SRG.  Readmission destination and risk of mortality after major surgery: an observational cohort study.  Lancet. 2015;386(9996):884-895. doi:10.1016/S0140-6736(15)60087-3PubMedGoogle Scholar
68.
Garabedian  LF, Chu  P, Toh  S, Zaslavsky  AM, Soumerai  SB.  Potential bias of instrumental variable analyses for observational comparative effectiveness research.  Ann Intern Med. 2014;161(2):131-138. doi:10.7326/M13-1887PubMedGoogle Scholar
69.
Finks  JF, Osborne  NH, Birkmeyer  JD.  Trends in hospital volume and operative mortality for high-risk surgery.  N Engl J Med. 2011;364(22):2128-2137. doi:10.1056/NEJMsa1010705PubMedGoogle Scholar
70.
Urbach  DR.  Pledging to eliminate low-volume surgery.  N Engl J Med. 2015;373(15):1388-1390. doi:10.1056/NEJMp1508472PubMedGoogle Scholar
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Views 3,393
    Original Investigation
    Surgery
    September 7, 2018

    Comparing Long-term Mortality After Carotid Endarterectomy vs Carotid Stenting Using a Novel Instrumental Variable Method for Risk Adjustment in Observational Time-to-Event Data

    Author Affiliations
    • 1The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, New Hampshire
    • 2Section of Vascular Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
    • 3Department of Biomedical Data Science, Geisel School of Medicine, Lebanon, New Hampshire
    • 4Department of Economics, Dartmouth College, Hanover, New Hampshire
    JAMA Netw Open. 2018;1(5):e181676. doi:10.1001/jamanetworkopen.2018.1676
    Key Points español 中文 (chinese)

    Question  Can a novel instrumental variable method designed for time-dependent outcomes more accurately determine the relative long-term mortality after carotid endarterectomy vs carotid artery stenting?

    Findings  In this registry-based, multicenter cohort study of 86 017 patients, the hazard ratio of long-term mortality for carotid endarterectomy vs carotid artery stenting was 0.83 (95% CI, 0.70-0.98) using instrumental variable analysis, compared with 0.69 (95% CI, 0.65-0.74) using a traditional Cox regression model.

    Meaning  Results from this instrumental variable method show that the survival advantage conferred by carotid endarterectomy is more modest than suggested by traditional adjustment methods, aligning with results from randomized clinical trials.

    Abstract

    Importance  Choosing between competing treatment options is difficult for patients and clinicians when results from randomized and observational studies are discordant. Observational real-world studies yield more generalizable evidence for decision making than randomized clinical trials, but unmeasured confounding, especially in time-to-event analyses, can limit validity.

    Objectives  To compare long-term survival after carotid endarterectomy (CEA) and carotid artery stenting (CAS) in real-world practice using a novel instrumental variable method designed for time-to-event outcomes, and to compare the results with traditional risk-adjustment models used in observational research for survival analyses.

    Design, Setting, and Participants  A multicenter cohort study was performed. The Vascular Quality Initiative, an observational quality improvement registry, was used to compare long-term mortality after CEA vs CAS. The study included 86 017 patients who underwent CEA (n = 73 312) or CAS (n = 12 705) between January 1, 2003, and December 31, 2016. Patients were followed up for long-term mortality assessment by linking the registry data to Medicare claims. Medicare claims data were available through September 31, 2015.

    Exposure  Procedure type (CEA vs CAS).

    Main Outcomes and Measures  The hazard ratios (HRs) of all-cause mortality using unadjusted, adjusted, propensity-matched, and instrumental variable methods were examined. The instrumental variable was the proportion of CEA among the total carotid procedures (endarterectomy and stenting) performed at each hospital in the 12 months before each patient’s index operation and therefore varies over the study period.

    Results  Participants who underwent CEA had a mean (SD) age of 70.3 (9.4) years compared with 69.1 (10.4) years for CAS, and most were men (44 191 [60.4%] for CEA and 8117 [63.9%] for CAS). The observed 5-year mortality was 12.8% (95% CI, 12.5%-13.2%) for CEA and 17.0% (95% CI, 16.0%-18.1%) for CAS. The unadjusted HR of mortality for CEA vs CAS was 0.67 (95% CI, 0.64-0.71), and Cox-adjusted and propensity-matched HRs were similar (0.69; 95% CI, 0.65-0.74 and 0.71; 95% CI, 0.65-0.77, respectively). These findings are comparable with published observational studies of CEA vs CAS. However, the association between CEA and mortality was more modest when estimated by instrumental variable analysis (HR, 0.83; 95% CI, 0.70-0.98), a finding similar to data reported in randomized clinical trials.

    Conclusions and Relevance  The study found a survival advantage associated with CEA over CAS in unadjusted and Cox-adjusted analyses. However, this finding was more modest when using an instrumental variable method designed for time-to-event outcomes for risk adjustment. The instrumental variable-based results were more similar to findings from randomized clinical trials, suggesting this method may provide less biased estimates of time-dependent outcomes in observational analyses.

    Introduction

    Randomized clinical trials, which have internal validity and test efficacy under carefully designed study conditions, produce findings that are often widely accepted.1-4 When results of observational studies are concordant with randomized clinical trials, clear messages emerge for patients, clinicians, and payers to guide treatment decisions.5-8 Concordance of results is particularly important when assessing a long-term, time-to-event outcome such as mortality, as this suggests that the findings seen in randomized clinical trials will be durable in clinical practice.9-11

    Treatment decisions are more difficult, however, when the results of randomized clinical trials and observational studies are discordant. For example, for patients and clinicians considering carotid endarterectomy (CEA) or carotid artery stenting (CAS), 2 competing treatments to prevent stroke from carotid artery stenosis, long-term survival after the procedure remains a matter of debate. While randomized clinical trials have shown no statistically significant difference in mortality between the 2 procedures, observational evidence suggests survival following endarterectomy is superior.12-17

    Several potential explanations exist for this type of discordance.18 For example, treatment regimens and effects in randomized clinical trials may not reflect clinical practice, thereby limiting generalizability.19,20 This limitation of randomized clinical trials as well as their high cost and complexity make observational studies an attractive alternative. However, risk adjustment for confounding in observational data remains challenging. While methods such as Cox proportional hazards regression and propensity score matching have been developed to adjust observational time-to-event data for measured confounding, the possibility that unmeasured or even unmeasurable confounding persists in observational analyses is an important concern faced by patients and clinicians.11,21-24 Unmeasured confounding is of particular importance in patients with peripheral arterial disease considering invasive vs minimally invasive options, where surgeon selection bias and patient fitness for surgery have been shown to have an important association with clinical outcomes after aortic aneurysm repair.25 Selection bias and unmeasured confounding are likely to also occur in patients with carotid artery disease, where the decision to choose an invasive vs minimally invasive procedure is influenced by many factors.

    Instrumental variable analysis is a procedure unique in its ability to account for unmeasured confounding, and this method has been applied to linear and logistic regression models to evaluate outcomes that are not time dependent.26,27 To date, adaptation of instrumental variable methods in areas of medicine such as cardiovascular disease that often examine time-to-event data using Cox regression as the standard analytic tool has been limited.22,28-31 We recently developed an instrumental variable procedure for use with the Cox model and have shown that it outperforms the traditional Cox model and 2-stage approaches that include the Cox model.24,32 We apply this procedure to adjust for suspected unmeasured confounding when comparing individuals’ long-term mortality between 2 competing treatments for carotid revascularization in a large observational data set.

    Methods
    Data Sources

    Our analyses use data derived from the Vascular Quality Initiative registry, a national quality improvement registry that captures data on vascular procedures from more than 400 hospitals and practices across the United States and Canada.33 Patients and procedures entered in the registry were linked to the Medicare Denominator File for mortality assessment.34,35 This database includes patient-level information on baseline demographics, comorbid conditions, presenting neurologic symptoms, operative management, and mortality on patients who underwent CEA and CAS. Data from the Vascular Quality Initiative were available from January 1, 2003, to December 31, 2016. Medicare data were available until September 2015. All data were collected under the auspices of an Agency for Healthcare Research and Quality–designated Patient Safety Organization and were deidentified. Our study was approved by the Center for the Protection of Human Subjects at Dartmouth; a waiver of participant consent was obtained. This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.36

    Primary Exposure and Outcome

    The primary exposure of interest was procedure type (CEA vs CAS). These procedures represent the most common methods of carotid revascularization in current practice.37 Patients receiving more than 1 procedure type in the same day were assigned to the first procedure they received. Patients receiving repeated revascularization procedures during follow-up were assigned to the index procedure.

    The primary outcome was all-cause mortality. This was assessed for all patients in the registry using the Social Security Death Index. Patients were assessed from the time of their index procedure until death, and censored only at the end of their known follow-up period. Patients eligible for Medicare in the registry were linked to their respective Medicare claims file, analyzed through the end of September 2015. Successful linking was obtained in 92% and 91% of eligible patients who underwent CEA or CAS, respectively.

    Statistical Analysis

    We performed a sensitivity analysis with results stratified based on the presence or absence of focal neurologic symptoms (symptomatic vs asymptomatic) at the time of presentation. We used clinical variables from the Vascular Quality Initiative to group patients as symptomatic or asymptomatic. We defined patients as symptomatic if they had a documented stroke, transient ischemic attack, or other ischemic neurologic symptoms at the time of hospitalization for their index procedure.

    We reported unadjusted mortality as absolute and relative frequencies where appropriate. We calculated the hazard ratio (HR) of mortality for CEA vs CAS using 4 methods of estimation: unadjusted, Cox regression adjusted, propensity matched, and our instrumental variable procedure designed for time-to-event data. We applied these to the overall cohort, as well as in the sensitivity analysis based on the presence of focal neurologic symptoms at the time of revascularization. Statistical tests were 2-sided with P < .05 considered significant. All statistical analyses were performed with R, version 3.3.2 (R Foundation for Statistical Computing).

    Unadjusted and Adjusted Mortality

    We calculated unadjusted mortality rates using Kaplan-Meier estimation. We then used Cox regression to estimate the HR of postoperative mortality for CEA vs CAS to account for observed confounding.24,38 Summary statistics for the confounding variables in the statistical models are noted in Table 1.

    Propensity-Matched Analysis

    We created a propensity-matched cohort balanced in baseline covariates.23,39 Using the observed covariates in Table 1, we created a logistic regression model in which the dependent variable was the treatment exposure (CEA vs CAS). Next, we calculated the fitted probability of CEA, known as the propensity score, for each patient. We then matched patients undergoing CEA to those undergoing CAS. We compared mortality between patients who underwent CEA vs CAS in the matched cohort. To account for the censoring, we applied Cox regression to our propensity-matched cohort to estimate the HR of mortality for CEA vs CAS.

    Instrumental Variable Analysis

    Our instrument was the proportion of CEA among the total number of carotid revascularization procedures (CEA and CAS) performed at each hospital in the 12 months prior to the index operation. We excluded hospitals not performing at least 10 revascularization procedures in the year prior to the index operation. In the presenting symptoms sensitivity analysis (patients presenting with focal neurologic symptoms vs not), we further excluded hospitals not performing at least 10 carotid revascularization procedures for each indication. For this reason, the number of patients included in the overall analysis slightly exceeds the total number of patients included in the sensitivity analysis.

    The instrumental variable procedure identifies patients who would have undergone CEA at some institutions and CAS at others based on the value of the instrument alone and not on patient characteristics.40 If patients choose hospitals based on convenience, or at least based only on observed factors, then where a patient seeks treatment emulates a randomized encouragement design in which assignment to a hospital with a historical precedent for performing a high proportion of CEA randomly exposes the patient to a greater likelihood of undergoing CEA.41 The estimation of treatment effects using instrumental variables is well developed for linear or logistic regression models.40,42,43 However, Cox proportional hazard models examining time-to-event outcomes selects on survivors over the course of follow-up, which is problematic for standard methods of instrumental variable identification.24,44-46 Therefore, we used the new instrumental variable estimator for the Cox proportional hazards model to simultaneously deal with the problems of unmeasured confounding and censoring of the outcome.32 We used this new procedure to examine the HR for all-cause mortality after CEA vs CAS by including both the instrument and all known confounding variables described in Table 1. The mean (SD) value of the instrumental variable was 0.89 (0.12) for patients undergoing CEA and 0.65 (0.29) for patients undergoing CAS (P < .001). Further details on derivation of the instrumental variable and its distribution can be found in the eMethods and eFigure 1 in the Supplement.

    Instrument Assessment

    We measured the strength of our instrument by determining if increasing levels of the instrument were associated with changing levels of the exposure.47 This is reported using the F statistic, for which a value greater than 10 traditionally indicates acceptable strength.40 The F statistic assesses the instrument’s ability to show association with the exposure received beyond the effect of any covariates that are adjusted for the survival model.

    Results
    Cohort Characteristics

    We studied 86 017 patients who underwent carotid revascularization (CEA, n = 73 312; CAS, n = 12 705) from January 1, 2003, to December 31, 2016. Mean follow-up was 3.0 (SD, 2.4 years; range, <0.1-14.3 years; median [interquartile range] follow-up, 2.5 [1.3-4.0] years), yielding the equivalent of 259 700 person-years for analysis. Vital status was known for 75.0% of patients who were eligible (procedure date, 2011 or earlier). Compared with patients who underwent CEA, those who underwent CAS tended to be younger (mean [SD] age, 70.3 [9.4] vs 69.1 [10.4] years, respectively), were more likely to be male (44 191 [60.4%] vs 8117 [63.9%], respectively), and were more likey to have an urgent procedure (2453 [19.3%] vs 9290 [12.7%], respectively) (Table 1). More than 89% of patients were receiving some form of antiplatelet therapy, and more than 75% were receiving a statin. Characteristics of the sensitivity analysis cohorts (symptomatic and asymptomatic patients) were similar (eTables 1 and 2 in the Supplement).

    There were several clinically meaningful differences between patients who underwent CEA and those who underwent CAS. Approximately one-third of patients undergoing CEA underwent the procedure because of focal neurologic symptoms, compared with more than half of patients treated with CAS. Patients who underwent CAS were also more likely to have several chronic comorbid conditions, including coronary artery disease, heart failure, and pulmonary disease. Patients who underwent CAS were also more likely to have previously undergone carotid surgery.

    Given that several differences existed in the characteristics between patients treated with CEA and CAS, we created a propensity-matched cohort for analysis. The propensity-matched cohort consisted of 12 340 matched pairs of patients and was well balanced in baseline characteristics apart from a small difference in aspirin use (84.0% in the CEA group and 85.5% in the CAS group; P = .001), β-blocker prescription (56.8% in the CEA group and 55.5% in the CAS group; P = .04), and in the proportion of procedures performed for symptomatic stenosis (51.3% in the CEA group and 52.8% in the CAS group; P = .02). A graphical representation of the performance of the propensity score matching can be found in eFigure 2 in the Supplement.

    Unadjusted, Cox-Adjusted, and Propensity-Matched Mortality by Procedure Type

    The unadjusted Kaplan-Meier estimate of all-cause mortality at 5 years for CEA was 12.8% (95% CI, 12.5%-13.2%) and for CAS was 17.0% (95% CI, 16.0%-18.1%; log rank, P < .001). At 10 years after the procedure, estimated mortality was 27.3% (95% CI, 26.3%-27.3%) for CEA and 27.4% (23.9%-30.7%) for CAS (log rank, P < .001; eFigure 3 in the Supplement). Sensitivity analysis by the presence of neurologic symptoms at the time of revascularization demonstrated similar findings (Figure 1).

    The unadjusted HR of all-cause mortality for CEA vs CAS was 0.67 (95% CI, 0.64-0.71) (Table 2). A Cox proportional hazards model adjusting for differences in patient characteristics showed a similar association (HR, 0.69; 95% CI, 0.65-0.74), further suggesting that CEA was associated with a survival advantage. The propensity-matched cohort also revealed a survival advantage associated with CEA (HR, 0.71; 95% CI, 0.65-0.77). Sensitivity analysis by the presence of neurologic symptoms before carotid revascularization continued to show a statistically significant association (Table 2 and Figure 2).16,48

    Instrumental Variable–Adjusted Mortality by Procedure Type

    The instrument, each individual hospital’s 12-month prior proportion of CEA procedures, demonstrated a very strong association with the type of carotid procedure performed (F = 18 631). Applying our instrumental variable procedure to all-cause mortality revealed that patients selected for CEA had a more modest survival advantage (HR, 0.83; 95% CI, 0.70-0.98) than was suggested by results of our other analytic methods. These results are similar to the findings of published randomized clinical trials (Figure 2). Similar results were obtained by our instrumental variable approach in a sensitivity analyses stratified by presenting symptoms, although the association was more pronounced in those who were symptomatic. The HRs for those with symptoms changed by an absolute 17% to 19% between traditional statistical methods and our instrumental variable model, compared with an absolute change of 11% to 14% in those who were asymptomatic (Table 2).

    Discussion

    In this observational study, unadjusted, adjusted, and propensity-matched models of long-term mortality all demonstrated that treatment with CEA was associated with a survival benefit relative to treatment with CAS. These results are comparable with published observational reports but conflict with randomized clinical trials, which suggest survival is similar following the 2 competing treatment options.12-17,49,50 Our instrumental variable method designed for risk adjustment of time-to-event data estimated a more modest association with long-term mortality, a finding consistent with the results of randomized clinical trials.16 These findings were robust to a sensitivity analysis by the presence of focal neurologic symptoms. This method, which accounts for both measured and unmeasured confounding in observational time-to-event analyses, represents an advance for investigators evaluating long-term outcomes, especially when considering clinical questions where randomized clinical trials are not possible or would be prohibitively expensive or when use of real-world evidence would be advantageous.51

    Discordance between randomized clinical trials and observational studies is neither new nor uncommon.52-57 For example, differences in the efficacy of vitamin E and hormone replacement therapy for the prevention of heart disease as well as antioxidant therapy for cancer represent important examples where conflicting results from randomized clinical trials and observational studies have affected evidence-based treatment decisions.52-56 Meta-analyses examining the relative findings of randomized and observational studies suggest that observational studies tend to generate a larger treatment effect, and these differences may be further potentiated when assessing long-term outcomes such as mortality.58-64 However, this is not always the case; a recent Cochrane review estimated that treatment effects were similar between randomized clinical trials and observational studies (pooled ratio of odds ratios, 1.04; 95% CI, 0.89-1.21).18 These contradictory results highlight how challenging it can be for patients and clinicians to interpret observational study results, especially if the direction of bias cannot be foreseen.

    In the example of discordance used in our analysis, patients with CAS, the Asymptomatic Carotid Trial (ACT 1) reported no statistically significant difference in 5-year mortality after CEA vs CAS,16 and the Carotid Revascularization Endarterectomy vs Stenting Trial (CREST) reported no statistically significant difference at 10 years in the composite outcome of long-term risk of stroke or perioperative myocardial infarction, stroke, or death.48 Despite these randomized clinical trials, several large observational studies have documented inferior outcomes for stenting overall, especially in subgroup analyses of symptomatic patients, which may bias physicians and patients away from choosing stenting as a procedural option.12-15,50 Our findings, which suggest that there is a modest association between survival and CEA, provide important granular detail to help inform this management decision.

    The design and execution of a randomized clinical trial that provides true, unbiased estimates is a very difficult task, as there are several threats to both the validity and generalizability of their results. In some cases, clinical trial participants may not be representative of the target population.18-20 In others, heterogeneous treatment effects may impede the ability of physicians to parse out which patients may benefit most from an intervention.18-20 In addition, intention-to-treat estimators used in randomized clinical trials may be biased toward the null if noncompliance is considerable.65 These limitations to many contemporary randomized clinical trials highlight the utility of observational studies where real-world evidence can be used, provided that adequate adjustment for confounding can be performed.

    An analytic technique capable of better risk adjustment for unmeasured confounding would improve the reliance that could be placed on results from observational studies. Such a technique would allow real-world observational data to more consistently reflect the true outcome of treatment independent of confounding. Our instrumental variable procedure was specifically designed to be used to analyze time-to-event outcomes.32 While determining a suitable instrument may be difficult in some settings, there appears to be few disadvantages in applying this procedure to observational questions with time-dependent outcomes such as mortality.22,47,66,67

    While the findings of our instrumental variable analyses gave results that are similar to those in randomized clinical trials, this may not be the case when applied to other clinical scenarios. The importance of observational data is that it documents results from clinical practice, outside of the confines of randomized clinical trials. Observational studies often include a much broader patient population with treatment-effect heterogeneity than are found in randomized clinical trials.20 In these situations, results from instrumental variable analyses may be different than those found in randomized clinical trials and may better represent the results that can be expected when an intervention is incorporated into clinical practice. Application of instrumental variables to time-to-event data therefore represents an important step forward in the evaluation of interventions in contemporary practice.

    Limitations

    Our study had limitations. First, it is not possible to truly know whether our instrumental variable balanced all unmeasured confounding. However, our sensitivity analyses by the presence of neurologic symptoms are reassuring. One would anticipate that unmeasured confounding would have a greater impact on the symptomatic analysis as patients in this subgroup are frequently sicker and thus are at higher risk for clinician selection bias to play a substantial role in the treatment decision. An instrument that accounts for unmeasurable confounding would change the effect size to a greater extent in these patients, and this was noted in our analyses. Second, we did not examine stroke-free survival as our primary outcome because of the heterogeneity in stroke assessment methods across the sites in our observational registry, an issue not encountered when examining survival as an outcome. Third, while 5-year vital status was known in 75.0% of patients who were eligible, many patients were not eligible for this assessment because of the date of their procedure (after 2011). Changes over time in both practice patterns and procedural competency may have an impact on the HR of mortality between the 2 procedures. However, findings remained consistent among patients who had their operations in earlier years where the longest follow-up was possible. Therefore, we feel that our estimates reported herein are an accurate reflection of long-term mortality after CEA vs CAS. In addition, instrumental variables must satisfy three conditions: first, they must be associated with the treatment exposure; second, an instrument must have no relationship to the outcome except through the effect on the exposure; and third, there must be no variables that affect both the instrument and the outcome.68 Our F statistic demonstrated that our instrument was strongly associated with the treatment exposure, thereby satisfying the first condition. It is not possible to prove whether an instrument is unrelated to an outcome. However, we required that a center perform at least 10 CEA or CAS procedures in the prior year to have patients included in the instrumental variable analysis to limit the possibility that proportion of procedures performed could be related to postoperative mortality.69,70

    Conclusions

    Using a novel instrumental variable method designed for time-to-event data, we found only a modest difference in long-term mortality after CEA vs CAS, a result that is comparable with recent randomized clinical trials. These similarities provide evidence that results from our instrumental variable procedure are more closely aligned with the true relative long-term mortality between the 2 revascularization procedures than incumbent methods for analyzing observational data. This method, which allows instruments to be used for risk adjustment with the widely used Cox regression model, may improve the validity of results for time-dependent outcomes for clinical questions where randomized clinical trials are not possible or would be prohibitively expensive or when use of real-world evidence would be advantageous.

    Back to top
    Article Information

    Accepted for Publication: June 7, 2018.

    Published: September 7, 2018. doi:10.1001/jamanetworkopen.2018.1676

    Correction: This article was corrected on October 12, 2018, to fix errors in the title and Figure 2.

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Columbo JA et al. JAMA Network Open.

    Corresponding Author: A. James O’Malley, PhD, The Dartmouth Institute for Health Policy and Clinical Practice, One Medical Center Drive, Lebanon, NH 03756 (james.omalley@dartmouth.edu).

    Author Contributions: Drs O’Malley and Martinez-Camblor had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: Columbo, Martinez-Camblor, Mackenzie, Staiger, Goodney, O’Malley.

    Drafting of the manuscript: Columbo, Goodney, O’Malley.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Martinez-Camblor, Mackenzie, Staiger, O’Malley.

    Obtained funding: O’Malley.

    Administrative, technical, or material support: Columbo, Kang, Goodney, O’Malley.

    Supervision: Columbo, O’Malley.

    Conflict of Interest Disclosures: Dr Staiger reported grants from the Patient-Centered Outcomes Research Institute during the conduct of the study; and personal fees, nonfinancial support, and grants from ArborMetrix, Inc outside the submitted work. Dr O’Malley reported receiving grants from the Patient-Centered Outcomes Research Institute and the National Institutes of Health during the conduct of the study. No other disclosures were reported.

    Funding/Support: This work was supported by Patient-Centered Outcomes Research Institute award ME-1503-28261. Additional support was provided by the US Food and Drug Administration (U01-FD005478), the National Institute on Aging (PO1-AG019783), and the National Institutes of Health Common Fund (U01-AG046830).

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: All statements in this article, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute, its Board of Governors, or Methodology Committee.

    References
    1.
    Barnett  HJM, Taylor  DW, Haynes  RB,  et al; North American Symptomatic Carotid Endarterectomy Trial Collaborators.  Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis.  N Engl J Med. 1991;325(7):445-453. doi:10.1056/NEJM199108153250701PubMedGoogle ScholarCrossref
    2.
     Endarterectomy for asymptomatic carotid artery stenosis: executive committee for the Asymptomatic Carotid Atherosclerosis Study.  JAMA. 1995;273(18):1421-1428. doi:10.1001/jama.1995.03520420037035PubMedGoogle ScholarCrossref
    3.
    Halliday  A, Mansfield  A, Marro  J,  et al; MRC Asymptomatic Carotid Surgery Trial Collaborative Group.  Prevention of disabling and fatal strokes by successful carotid endarterectomy in patients without recent neurological symptoms: randomised controlled trial.  Lancet. 2004;363(9420):1491-1502. doi:10.1016/S0140-6736(04)16146-1PubMedGoogle ScholarCrossref
    4.
    Brott  TG, Halperin  JL, Abbara  S,  et al; American College of Cardiology Foundation; American Stroke Association; American Association of Neurological Surgeons; American College of Radiology; American Society of Neuroradiology; Congress of Neurological Surgeons; Society of Atherosclerosis Imaging and Prevention; Society for Cardiovascular Angiography and Interventions; Society of Interventional Radiology; Society of NeuroInterventional Surgery; Society for Vascular Medicine; Society for Vascular Surgery.  2011 ASA/ACCF/AHA/AANN/AANS/ACR/ASNR/CNS/SAIP/SCAI/SIR/SNIS/SVM/SVS guideline on the management of patients with extracranial carotid and vertebral artery disease: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American Stroke Association, American Association of Neuroscience Nurses, American Association of Neurological Surgeons, American College of Radiology, American Society of Neuroradiology, Congress of Neurological Surgeons, Society of Atherosclerosis Imaging and Prevention, Society for Cardiovascular Angiography and Interventions, Society of Interventional Radiology, Society of NeuroInterventional Surgery, Society for Vascular Medicine, and Society for Vascular Surgery.  Circulation. 2011;124(4):489-532. doi:10.1161/CIR.0b013e31820d8d78PubMedGoogle ScholarCrossref
    5.
    Manson  JE, Stampfer  MJ, Colditz  GA,  et al.  A prospective study of aspirin use and primary prevention of cardiovascular disease in women.  JAMA. 1991;266(4):521-527. doi:10.1001/jama.1991.03470040085027PubMedGoogle ScholarCrossref
    6.
    Kannel  WB, Larson  M.  Long-term epidemiologic prediction of coronary disease: the Framingham experience.  Cardiology. 1993;82(2-3):137-152. doi:10.1159/000175864PubMedGoogle ScholarCrossref
    7.
    Baigent  C, Blackwell  L, Collins  R,  et al; Antithrombotic Trialists’ Collaboration.  Aspirin in the primary and secondary prevention of vascular disease: collaborative meta-analysis of individual participant data from randomised trials.  Lancet. 2009;373(9678):1849-1860. doi:10.1016/S0140-6736(09)60503-1PubMedGoogle ScholarCrossref
    8.
    US Preventive Services Task Force.  Aspirin for the prevention of cardiovascular disease: U.S. Preventive Services Task Force recommendation statement.  Ann Intern Med. 2009;150(6):396-404. doi:10.7326/0003-4819-150-6-200903170-00008PubMedGoogle ScholarCrossref
    9.
    Lederle  FA, Freischlag  JA, Kyriakides  TC,  et al; Open Versus Endovascular Repair Veterans Affairs Cooperative Study Group.  Outcomes following endovascular vs open repair of abdominal aortic aneurysm: a randomized trial.  JAMA. 2009;302(14):1535-1542. doi:10.1001/jama.2009.1426PubMedGoogle ScholarCrossref
    10.
    De Bruin  JL, Baas  AF, Buth  J,  et al; DREAM Study Group.  Long-term outcome of open or endovascular repair of abdominal aortic aneurysm.  N Engl J Med. 2010;362(20):1881-1889. doi:10.1056/NEJMoa0909499PubMedGoogle ScholarCrossref
    11.
    Schermerhorn  ML, Buck  DB, O’Malley  AJ,  et al.  Long-Term outcomes of abdominal aortic aneurysm in the medicare population.  N Engl J Med. 2015;373(4):328-338. doi:10.1056/NEJMoa1405778PubMedGoogle ScholarCrossref
    12.
    Giles  KA, Hamdan  AD, Pomposelli  FB, Wyers  MC, Schermerhorn  ML.  Stroke and death after carotid endarterectomy and carotid artery stenting with and without high risk criteria.  J Vasc Surg. 2010;52(6):1497-1504. doi:10.1016/j.jvs.2010.06.174PubMedGoogle ScholarCrossref
    13.
    McPhee  JT, Schanzer  A, Messina  LM, Eslami  MH.  Carotid artery stenting has increased rates of postprocedure stroke, death, and resource utilization than does carotid endarterectomy in the United States, 2005.  J Vasc Surg. 2008;48(6):1442-1450, 1450e1. doi:10.1016/j.jvs.2008.07.017PubMedGoogle ScholarCrossref
    14.
    Wang  FW, Esterbrooks  D, Kuo  YF, Mooss  A, Mohiuddin  SM, Uretsky  BF.  Outcomes after carotid artery stenting and endarterectomy in the Medicare population.  Stroke. 2011;42(7):2019-2025. doi:10.1161/STROKEAHA.110.608992PubMedGoogle ScholarCrossref
    15.
    Nolan  BW, De Martino  RR, Goodney  PP,  et al; Vascular Study Group of New England.  Comparison of carotid endarterectomy and stenting in real world practice using a regional quality improvement registry.  J Vasc Surg. 2012;56(4):990-996. doi:10.1016/j.jvs.2012.03.009PubMedGoogle ScholarCrossref
    16.
    Rosenfield  K, Matsumura  JS, Chaturvedi  S,  et al; ACT I Investigators.  Randomized trial of stent versus surgery for asymptomatic carotid stenosis.  N Engl J Med. 2016;374(11):1011-1020. doi:10.1056/NEJMoa1515706PubMedGoogle ScholarCrossref
    17.
    Bonati  LH, Dobson  J, Featherstone  RL,  et al; International Carotid Stenting Study Investigators.  Long-term outcomes after stenting versus endarterectomy for treatment of symptomatic carotid stenosis: the International Carotid Stenting Study (ICSS) randomised trial.  Lancet. 2015;385(9967):529-538. doi:10.1016/S0140-6736(14)61184-3PubMedGoogle ScholarCrossref
    18.
    Anglemyer  A, Horvath  HT, Bero  L.  Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials.  Cochrane Database Syst Rev. 2014;(4):MR000034.PubMedGoogle Scholar
    19.
    Rothwell  PM.  External validity of randomised controlled trials: “to whom do the results of this trial apply?”  Lancet. 2005;365(9453):82-93. doi:10.1016/S0140-6736(04)17670-8PubMedGoogle ScholarCrossref
    20.
    Dhruva  SS, Redberg  RF.  Variations between clinical trial participants and Medicare beneficiaries in evidence used for Medicare national coverage decisions.  Arch Intern Med. 2008;168(2):136-140. doi:10.1001/archinternmed.2007.56PubMedGoogle ScholarCrossref
    21.
    Grimes  DA, Schulz  KF.  Bias and causal associations in observational research.  Lancet. 2002;359(9302):248-252. doi:10.1016/S0140-6736(02)07451-2PubMedGoogle ScholarCrossref
    22.
    Stukel  TA, Fisher  ES, Wennberg  DE, Alter  DA, Gottlieb  DJ, Vermeulen  MJ.  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.  JAMA. 2007;297(3):278-285. doi:10.1001/jama.297.3.278PubMedGoogle Scholar
    23.
    D’Agostino  RB  Jr.  Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.  Stat Med. 1998;17(19):2265-2281. doi:10.1002/(SICI)1097-0258(19981015)17:19<2265::AID-SIM918>3.0.CO;2-BPubMedGoogle Scholar
    24.
    Cox  DR.  Regression models and life-tables. J R Stat Soc B. 1972;34(2):187-220. http://www.jstor.org/stable/2985181. Accessed September 1, 2017.
    25.
    De Martino  RR, Brooke  BS, Robinson  W,  et al.  Designation as “unfit for open repair” is associated with poor outcomes after endovascular aortic aneurysm repair.  Circ Cardiovasc Qual Outcomes. 2013;6(5):575-581. doi:10.1161/CIRCOUTCOMES.113.000303PubMedGoogle Scholar
    26.
    Hernán  MA, Robins  JM.  Instruments for causal inference: an epidemiologist’s dream?  Epidemiology. 2006;17(4):360-372. doi:10.1097/01.ede.0000222409.00878.37PubMedGoogle Scholar
    27.
    Terza  JV, Basu  A, Rathouz  PJ.  Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling.  J Health Econ. 2008;27(3):531-543. doi:10.1016/j.jhealeco.2007.09.009PubMedGoogle Scholar
    28.
    MacKenzie  TA, Tosteson  TD, Morden  NE, Stukel  TA, O’Malley  AJ.  Using instrumental variables to estimate a Cox’s proportional hazards regression subject to additive confounding.  Health Serv Outcomes Res Methodol. 2014;14(1-2):54-68. doi:10.1007/s10742-014-0117-xPubMedGoogle Scholar
    29.
    Aalen  OO, Cook  RJ, Røysland  K.  Does Cox analysis of a randomized survival study yield a causal treatment effect?  Lifetime Data Anal. 2015;21(4):579-593. doi:10.1007/s10985-015-9335-yPubMedGoogle Scholar
    30.
    Li  J, Fine  J, Brookhart  A.  Instrumental variable additive hazards models.  Biometrics. 2015;71(1):122-130. doi:10.1111/biom.12244PubMedGoogle Scholar
    31.
    Martinussen  T, Nørbo Sørensen  D, Vansteelandt  S.  Instrumental variables estimation under a structural Cox model.  Biostatistics. 2017.PubMedGoogle Scholar
    32.
    Martínez-Camblor  P, Mackenzie  T, Staiger  DO, Goodney  PP, O’Malley  AJ.  Adjusting for bias introduced by instrumental variable estimation in the Cox proportional hazards model.  Biostatistics. 2017. doi:10.1093/biostatistics/kxx062PubMedGoogle Scholar
    33.
    Vascular Quality Initiative. http://www.vascularqualityinitiative.org/. Accessed March 1, 2017.
    34.
    Centers for Medicare and Medicaid Services. http://www.cms.gov. Accessed March 17, 2017.
    35.
    Hoel  AW, Faerber  AE, Moore  KO,  et al.  A pilot study for long-term outcome assessment after aortic aneurysm repair using vascular quality initiative data matched to Medicare claims.  J Vasc Surg. 2017;66(3):751-759.e1. doi:10.1016/j.jvs.2016.12.100PubMedGoogle Scholar
    36.
    Vandenbroucke  JP, von Elm  E, Altman  DG,  et al; STROBE Initiative.  Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.  PLoS Med. 2007;4(10):e297. doi:10.1371/journal.pmed.0040297PubMedGoogle Scholar
    37.
    Skerritt  MR, Block  RC, Pearson  TA, Young  KC.  Carotid endarterectomy and carotid artery stenting utilization trends over time.  BMC Neurol. 2012;12:17. doi:10.1186/1471-2377-12-17PubMedGoogle Scholar
    38.
    Cortese  G, Scheike  TH, Martinussen  T.  Flexible survival regression modelling.  Stat Methods Med Res. 2010;19(1):5-28. doi:10.1177/0962280209105022PubMedGoogle Scholar
    39.
    Rosenbaum  PR, Rubin  DB.  Constructing a control group using multivariate matched sampling methods that incorporate the propensity score.  Am Stat. 1985;39(1):33-38. doi:10.2307/2683903Google Scholar
    40.
    Stock  J, Yogo  M. Testing for weak instruments in linear IV regression. In: Andrews  DWK, ed.  Identification and Inference for Econometric Models. New York, NY: Cambridge University Press; 2005:80-108. doi:10.1017/CBO9780511614491.006
    41.
    West  SG, Duan  N, Pequegnat  W,  et al.  Alternatives to the randomized controlled trial.  Am J Public Health. 2008;98(8):1359-1366. doi:10.2105/AJPH.2007.124446PubMedGoogle Scholar
    42.
    Staiger  D, Stock  JH.  Instrumental variables regression with weak instruments.  Econometrica. 1997;65(3):557-586. doi:10.2307/2171753Google Scholar
    43.
    Rassen  JA, Schneeweiss  S, Glynn  RJ, Mittleman  MA, Brookhart  MA.  Instrumental variable analysis for estimation of treatment effects with dichotomous outcomes.  Am J Epidemiol. 2009;169(3):273-284. doi:10.1093/aje/kwn299PubMedGoogle Scholar
    44.
    Wan  F, Small  D, Bekelman  JE, Mitra  N.  Bias in estimating the causal hazard ratio when using two-stage instrumental variable methods.  Stat Med. 2015;34(14):2235-2265. doi:10.1002/sim.6470PubMedGoogle Scholar
    45.
    Cai  B, Small  DS, Have  TR.  Two-stage instrumental variable methods for estimating the causal odds ratio: analysis of bias.  Stat Med. 2011;30(15):1809-1824. doi:10.1002/sim.4241PubMedGoogle Scholar
    46.
    MacKenzie  TA, Brown  JR, Likosky  DS, Wu  Y, Grunkemeier  GL.  Review of case-mix corrected survival curves.  Ann Thorac Surg. 2012;93(5):1416-1425. doi:10.1016/j.athoracsur.2011.12.094PubMedGoogle Scholar
    47.
    Tan  HJ, Norton  EC, Ye  Z, Hafez  KS, Gore  JL, Miller  DC.  Long-term survival following partial vs radical nephrectomy among older patients with early-stage kidney cancer.  JAMA. 2012;307(15):1629-1635. doi:10.1001/jama.2012.475PubMedGoogle Scholar
    48.
    Brott  TG, Howard  G, Roubin  GS,  et al; CREST Investigators.  Long-term results of stenting versus endarterectomy for carotid-artery stenosis.  N Engl J Med. 2016;374(11):1021-1031. doi:10.1056/NEJMoa1505215PubMedGoogle Scholar
    49.
    Paraskevas  KI, Kalmykov  EL, Naylor  AR.  Stroke/death rates following carotid artery stenting and carotid endarterectomy in contemporary administrative dataset registries: a systematic review.  Eur J Vasc Endovasc Surg. 2016;51(1):3-12. doi:10.1016/j.ejvs.2015.07.032PubMedGoogle Scholar
    50.
    Timaran  CH, Veith  FJ, Rosero  EB, Modrall  JG, Valentine  RJ, Clagett  GP.  Intracranial hemorrhage after carotid endarterectomy and carotid stenting in the United States in 2005.  J Vasc Surg. 2009;49(3):623-628. doi:10.1016/j.jvs.2008.09.064PubMedGoogle Scholar
    51.
    US Department of Health and Human Services, US Food and Drug Administration.  Use of real-world evidence to support regulatory decision-making for Medical devices. https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm513027.pdf. Accessed September 1, 2017.
    52.
    Menkes  MS, Comstock  GW, Vuilleumier  JP, Helsing  KJ, Rider  AA, Brookmeyer  R.  Serum beta-carotene, vitamins A and E, selenium, and the risk of lung cancer.  N Engl J Med. 1986;315(20):1250-1254. doi:10.1056/NEJM198611133152003PubMedGoogle Scholar
    53.
    Stampfer  MJ, Colditz  GA.  Estrogen replacement therapy and coronary heart disease: a quantitative assessment of the epidemiologic evidence.  Prev Med. 1991;20(1):47-63. doi:10.1016/0091-7435(91)90006-PPubMedGoogle Scholar
    54.
    Alpha-Tocopherol, Beta Carotene Cancer Prevention Study Group.  The effect of vitamin E and beta carotene on the incidence of lung cancer and other cancers in male smokers.  N Engl J Med. 1994;330(15):1029-1035. doi:10.1056/NEJM199404143301501PubMedGoogle Scholar
    55.
    Khaw  KT, Bingham  S, Welch  A,  et al.  Relation between plasma ascorbic acid and mortality in men and women in EPIC-Norfolk prospective study: a prospective population study. European prospective investigation into cancer and nutrition.  Lancet. 2001;357(9257):657-663. doi:10.1016/S0140-6736(00)04128-3PubMedGoogle Scholar
    56.
    Beral  V, Banks  E, Reeves  G.  Evidence from randomised trials on the long-term effects of hormone replacement therapy.  Lancet. 2002;360(9337):942-944. doi:10.1016/S0140-6736(02)11032-4PubMedGoogle Scholar
    57.
    Heart Protection Study Collaborative Group.  MRC/BHF Heart Protection Study of antioxidant vitamin supplementation in 20,536 high-risk individuals: a randomised placebo-controlled trial.  Lancet. 2002;360(9326):23-33. doi:10.1016/S0140-6736(02)09328-5PubMedGoogle Scholar
    58.
    Deeks  JJ, Dinnes  J, D’Amico  R,  et al; International Stroke Trial Collaborative Group; European Carotid Surgery Trial Collaborative Group.  Evaluating non-randomised intervention studies.  Health Technol Assess. 2003;7(27):iii-x, 1-173. doi:10.3310/hta7270PubMedGoogle Scholar
    59.
    Kunz  R, Oxman  AD.  The unpredictability paradox: review of empirical comparisons of randomised and non-randomised clinical trials.  BMJ. 1998;317(7167):1185-1190. doi:10.1136/bmj.317.7167.1185PubMedGoogle Scholar
    60.
    Ioannidis  JP, Haidich  AB, Pappa  M,  et al.  Comparison of evidence of treatment effects in randomized and nonrandomized studies.  JAMA. 2001;286(7):821-830. doi:10.1001/jama.286.7.821PubMedGoogle Scholar
    61.
    Brewer  T, Colditz  GA.  Postmarketing surveillance and adverse drug reactions: current perspectives and future needs.  JAMA. 1999;281(9):824-829. doi:10.1001/jama.281.9.824PubMedGoogle Scholar
    62.
    Ross  SD.  Drug-related adverse events: a readers’ guide to assessing literature reviews and meta-analyses.  Arch Intern Med. 2001;161(8):1041-1046. doi:10.1001/archinte.161.8.1041PubMedGoogle Scholar
    63.
    Sutton  AJ, Cooper  NJ, Lambert  PC, Jones  DR, Abrams  KR, Sweeting  MJ.  Meta-analysis of rare and adverse event data.  Expert Rev Pharmacoecon Outcomes Res. 2002;2(4):367-379. doi:10.1586/14737167.2.4.367PubMedGoogle Scholar
    64.
    Ioannidis  JP, Mulrow  CD, Goodman  SN.  Adverse events: the more you search, the more you find.  Ann Intern Med. 2006;144(4):298-300. doi:10.7326/0003-4819-144-4-200602210-00013PubMedGoogle Scholar
    65.
    Newcombe  RG.  Explanatory and pragmatic estimates of the treatment effect when deviations from allocated treatment occur.  Stat Med. 1988;7(11):1179-1186. doi:10.1002/sim.4780071111PubMedGoogle Scholar
    66.
    Hearst  N, Newman  TB, Hulley  SB.  Delayed effects of the military draft on mortality: a randomized natural experiment.  N Engl J Med. 1986;314(10):620-624. doi:10.1056/NEJM198603063141005PubMedGoogle Scholar
    67.
    Brooke  BS, Goodney  PP, Kraiss  LW, Gottlieb  DJ, Samore  MH, Finlayson  SRG.  Readmission destination and risk of mortality after major surgery: an observational cohort study.  Lancet. 2015;386(9996):884-895. doi:10.1016/S0140-6736(15)60087-3PubMedGoogle Scholar
    68.
    Garabedian  LF, Chu  P, Toh  S, Zaslavsky  AM, Soumerai  SB.  Potential bias of instrumental variable analyses for observational comparative effectiveness research.  Ann Intern Med. 2014;161(2):131-138. doi:10.7326/M13-1887PubMedGoogle Scholar
    69.
    Finks  JF, Osborne  NH, Birkmeyer  JD.  Trends in hospital volume and operative mortality for high-risk surgery.  N Engl J Med. 2011;364(22):2128-2137. doi:10.1056/NEJMsa1010705PubMedGoogle Scholar
    70.
    Urbach  DR.  Pledging to eliminate low-volume surgery.  N Engl J Med. 2015;373(15):1388-1390. doi:10.1056/NEJMp1508472PubMedGoogle Scholar
    ×