Estimating treatment effect with clinical interpretation from a comparative clinical trial with an end point subject to competing risks

Lihui Zhao, Lu Tian, Brian Claggett, Marc Pfeffer, Dae Hyun Kim, Scott Solomon, Lee Jen Wei*

*Corresponding author for this work

Research output: Contribution to journalLetterpeer-review

24 Scopus citations
Original languageEnglish (US)
Pages (from-to)357-358
Number of pages2
JournalJAMA cardiology
Volume3
Issue number4
DOIs
StatePublished - Apr 2018

Funding

Author Affiliations: Department of Preventive Medicine, Northwestern University, Chicago, Illinois (Zhao); Stanford Medical School, Stanford University, Stanford, California (Tian); Division of Cardiovascular, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Claggett, Pfeffer, Solomon); Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Kim); Department of Biostatistics, Harvard University, Boston, Massachusetts (Wei). Corresponding Author: Lee-Jen Wei, PhD, Department of Biostatistics, Harvard University, 655 Huntington Ave, Boston, MA 02115 ([email protected]). Accepted for Publication: January 18, 2018. Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Pfeffer reports receiving personal fees from AstraZeneca, Bayer, Boehringer Ingelheim, DalCor, Gilead, GlaxoSmithKline, Janssen, Lilly USA, The Medicines Company, Merck, Novo Nordisk, Relypsa, Thrasos, Genzyme, and Teva and grants and personal fees from Novartis and Sanofi. He also holds a patent with Novartis for the use of inhibitors of renal artery stenosis in patients with myocardial infarction. Dr Kim reports receiving grant support from the National Institute on Aging and personal fees from Alosa Health. No other disclosures were reported. Published Online: March 14, 2018. doi:10.1001/jamacardio.2018.0127 Author Contributions: Drs Wei, Claggett, and Tian had full access to the data from the β-Blocker Evaluation of Survival Trial and had full responsibility for the integrity of data and the accuracy of data analysis. Drs Zhao and Tian equally contributed to this work and are co-first authors, and Drs Solomon and Wei have equally contributed to this work and are co-last authors. Concept and design: Zhao, Tian, Solomon, Wei. Acquisition,analysis,orinterpretationofdata:Zhao,Tian,Claggett,Pfeffer,Kim,Wei. Drafting of the manuscript: Zhao, Tian, Claggett, Wei. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Zhao, Tian. Obtained funding: Shao, Tian, Solomon, Wei. Supervision: Solomon, Wei. Funding/Support: This work was partially supported by grants and contracts from the National Institutes of Health. 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. Additional Contributions: We thank Michael Pencina, PhD, Duke Clinical Research Institute, and 3 reviewers for their constructive suggestions/ comments on the article. They were not compensated for their contributions. 1. Royston P, Parmar MK. Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event effect. BMC Med Res Methodol. 2013;13(1):152. 2. Uno H, Wittes J, Fu H, et al. Alternatives to hazard ratios for comparing the efficacy or safety of therapies in noninferiority studies. Ann Intern Med. 2015; 163(2):127-134. 3. Kim DH, Uno H, Wei LJ. Restricted mean survival time as a measure to interpret clinical trial results. JAMA Cardiol. 2017;2(11):1179-1180. 4. Eichhorn EJ, Domanski MJ, Krause-Steinrauf H, Bristow MR, Lavori PW; Beta-Blocker Evaluation of Survival Trial Investigators. A trial of the beta-blocker bucindolol in patients with advanced chronic heart failure. N Engl J Med. 2001; 344(22):1659-1667. 5. Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. Am J Epidemiol. 2009;170(2):244-256. 6. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496-509. doi:10.1080 /01621459.1999.10474144

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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