Literature DB >> 20031790

Primer on statistical interpretation or methods report card on propensity-score matching in the cardiology literature from 2004 to 2006: a systematic review.

Peter C Austin1.   

Abstract

BACKGROUND: Propensity-score matching is frequently used in the cardiology literature. Recent systematic reviews have found that this method is, in general, poorly implemented in the medical literature. The study objective was to examine the quality of the implementation of propensity-score matching in the general cardiology literature. METHODS AND
RESULTS: A total of 44 articles published in the American Heart Journal, the American Journal of Cardiology, Circulation, the European Heart Journal, Heart, the International Journal of Cardiology, and the Journal of the American College of Cardiology between January 1, 2004, and December 31, 2006, were examined. Twenty of the 44 studies did not provide adequate information on how the propensity-score-matched pairs were formed. Fourteen studies did not report whether matching on the propensity score balanced baseline characteristics between treated and untreated subjects in the matched sample. Only 4 studies explicitly used statistical methods appropriate for matched studies to compare baseline characteristics between treated and untreated subjects. Only 11 (25%) of the 44 studies explicitly used statistical methods appropriate for the analysis of matched data when estimating the effect of treatment on the outcomes. Only 2 studies described the matching method used, assessed balance in baseline covariates by appropriate methods, and used appropriate statistical methods to estimate the treatment effect and its significance.
CONCLUSIONS: Application of propensity-score matching was poor in the cardiology literature. Suggestions for improving the reporting and analysis of studies that use propensity-score matching are provided.

Mesh:

Year:  2008        PMID: 20031790     DOI: 10.1161/CIRCOUTCOMES.108.790634

Source DB:  PubMed          Journal:  Circ Cardiovasc Qual Outcomes        ISSN: 1941-7713


  54 in total

Review 1.  Do observational studies using propensity score methods agree with randomized trials? A systematic comparison of studies on acute coronary syndromes.

Authors:  Issa J Dahabreh; Radley C Sheldrick; Jessica K Paulus; Mei Chung; Vasileia Varvarigou; Haseeb Jafri; Jeremy A Rassen; Thomas A Trikalinos; Georgios D Kitsios
Journal:  Eur Heart J       Date:  2012-06-17       Impact factor: 29.983

2.  Uncontrolled hypertension and increased risk for incident heart failure in older adults with hypertension: findings from a propensity-matched prospective population study.

Authors:  Anand S Iyer; Mustafa I Ahmed; Gerasimos S Filippatos; O James Ekundayo; Inmaculada B Aban; Thomas E Love; Navin C Nanda; George L Bakris; Gregg C Fonarow; Wilbert S Aronow; Ali Ahmed
Journal:  J Am Soc Hypertens       Date:  2010 Jan-Feb

3.  Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses.

Authors:  Peter C Austin
Journal:  Int J Biostat       Date:  2009-04-14       Impact factor: 0.968

Review 4.  A systematic review of propensity score methods in the acute care surgery literature: avoiding the pitfalls and proposing a set of reporting guidelines.

Authors:  T L Zakrison; P C Austin; V A McCredie
Journal:  Eur J Trauma Emerg Surg       Date:  2017-03-24       Impact factor: 3.693

5.  Digoxin and 30-Day All-Cause Readmission in Long-Term Care Residents Hospitalized for Heart Failure.

Authors:  Helen M Sheriff; Manik R Thogaripally; Gurusher Panjrath; Cherinne Arundel; Qing Zeng; Gregg C Fonarow; Javed Butler; Ross D Fletcher; Charity Morgan; Marc R Blackman; Prakash Deedwania; Thomas E Love; Wilbert S Aronow; Stefan D Anker; Richard M Allman; Ali Ahmed
Journal:  J Am Med Dir Assoc       Date:  2017-05-11       Impact factor: 4.669

6.  Association between smoking and outcomes in older adults with atrial fibrillation.

Authors:  Pushkar P Pawar; Linda G Jones; Margaret Feller; Jason L Guichard; Marjan Mujib; Mustafa I Ahmed; Brita Roy; Toufiqur Rahman; Inmaculada B Aban; Thomas E Love; Michel White; Wilbert S Aronow; Gregg C Fonarow; Ali Ahmed
Journal:  Arch Gerontol Geriatr       Date:  2011-07-06       Impact factor: 3.250

Review 7.  A tutorial on methods to estimating clinically and policy-meaningful measures of treatment effects in prospective observational studies: a review.

Authors:  Peter C Austin; Andreas Laupacis
Journal:  Int J Biostat       Date:  2011-01-06       Impact factor: 0.968

8.  A propensity-matched study of the association of diabetes mellitus with incident heart failure and mortality among community-dwelling older adults.

Authors:  Brita Roy; Pushkar P Pawar; Ravi V Desai; Gregg C Fonarow; Marjan Mujib; Yan Zhang; Margaret A Feller; Fernando Ovalle; Inmaculada B Aban; Thomas E Love; Ami E Iskandrian; Prakash Deedwania; Ali Ahmed
Journal:  Am J Cardiol       Date:  2011-09-22       Impact factor: 2.778

9.  Sex differences in the treatment and outcome of patients with acute coronary syndrome after percutaneous coronary intervention: a population-based study.

Authors:  Chen-Fang Lin; Li-Jiuan Shen; Fei-Yuan Hsiao; Churn-Shiouh Gau; Fe-Lin Lin Wu
Journal:  J Womens Health (Larchmt)       Date:  2013-11-28       Impact factor: 2.681

10.  Impact of choice of imaging modality accompanying outpatient exercise stress testing on outcomes and resource use after revascularization for acute coronary syndromes.

Authors:  Jerome J Federspiel; Bimal R Shah; Leslee J Shaw; Frederick A Masoudi; Patricia P Chang; Sally C Stearns; Daniel W Mudrick; Patricia A Cowper; Cynthia L Green; Pamela S Douglas
Journal:  Am Heart J       Date:  2013-08-17       Impact factor: 4.749

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