Literature DB >> 10827374

Subgroups, treatment effects, and baseline risks: some lessons from major cardiovascular trials.

A B Parker1, C D Naylor.   

Abstract

BACKGROUND: The objective of this study was to determine how subgroup analyses are performed in large randomized trials of cardiovascular pharmacotherapy. METHODS AND
RESULTS: We reviewed 67 randomized, double-blind, controlled trials involving pharmacotherapy in at least 1000 patients with unstable angina, myocardial infarction, left ventricular dysfunction, or heart failure with clinical outcomes as primary end points, published between 1980 and 1997. Nine had no subgroup analyses but 43 reported on 5 or more subgroups and 31 reported subgroups without formal statistical tests for treatment-subgroup interactions. In most trials, a rationale for subgroup selection was missing. All but 6 focused on single-factor subgroups.
CONCLUSIONS: Trial subgroups should ideally be defined a priori on 2 bases: single-factor subgroups with a strong rationale for biological response modification and multifactorial prognostic subgroups defined from baseline risks. However, single-factor subgroup analyses are often reported without a supporting rationale or formal statistical tests for interactions. We suggest that clinicians should interpret published subgroup-specific variations in treatment effects skeptically unless there is a prespecified rationale and a significant treatment-subgroup interaction.

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Year:  2000        PMID: 10827374     DOI: 10.1067/mhj.2000.106610

Source DB:  PubMed          Journal:  Am Heart J        ISSN: 0002-8703            Impact factor:   4.749


  17 in total

1.  Interaction trees with censored survival data.

Authors:  Xiaogang Su; Tianni Zhou; Xin Yan; Juanjuan Fan; Song Yang
Journal:  Int J Biostat       Date:  2008-01-28       Impact factor: 0.968

2.  Access survival amongst hemodialysis patients referred for preventive angiography and percutaneous transluminal angioplasty.

Authors:  Kevin E Chan; Timothy A Pflederer; David J R Steele; Michael P Lilly; T Alp Ikizler; Frank W Maddux; Raymond M Hakim
Journal:  Clin J Am Soc Nephrol       Date:  2011-09-29       Impact factor: 8.237

Review 3.  Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects.

Authors:  David M Kent; Ewout Steyerberg; David van Klaveren
Journal:  BMJ       Date:  2018-12-10

4.  Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results?

Authors:  Erik Fernandez Y Garcia; Hien Nguyen; Naihua Duan; Nicole B Gabler; Richard L Kravitz
Journal:  Health Serv Res       Date:  2010-02       Impact factor: 3.402

Review 5.  Examining the evidence: a systematic review of the inclusion and analysis of older adults in randomized controlled trials.

Authors:  Donna M Zulman; Jeremy B Sussman; Xisui Chen; Christine T Cigolle; Caroline S Blaum; Rodney A Hayward
Journal:  J Gen Intern Med       Date:  2011-02-01       Impact factor: 5.128

6.  Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages.

Authors:  Richard L Kravitz; Naihua Duan; Joel Braslow
Journal:  Milbank Q       Date:  2004       Impact factor: 4.911

7.  Clinical prognosis, pre-existing conditions and the use of reperfusion therapy for patients with ST segment elevation acute myocardial infarction.

Authors:  Andrea B Parker; C David Naylor; Alice Chong; David A Alter
Journal:  Can J Cardiol       Date:  2006-02       Impact factor: 5.223

8.  The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.

Authors:  David M Kent; David van Klaveren; Jessica K Paulus; Ralph D'Agostino; Steve Goodman; Rodney Hayward; John P A Ioannidis; Bray Patrick-Lake; Sally Morton; Michael Pencina; Gowri Raman; Joseph S Ross; Harry P Selker; Ravi Varadhan; Andrew Vickers; John B Wong; Ewout W Steyerberg
Journal:  Ann Intern Med       Date:  2019-11-12       Impact factor: 25.391

9.  Dealing with heterogeneity of treatment effects: is the literature up to the challenge?

Authors:  Nicole B Gabler; Naihua Duan; Diana Liao; Joann G Elmore; Theodore G Ganiats; Richard L Kravitz
Journal:  Trials       Date:  2009-06-19       Impact factor: 2.279

10.  The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement.

Authors:  David M Kent; Jessica K Paulus; David van Klaveren; Ralph D'Agostino; Steve Goodman; Rodney Hayward; John P A Ioannidis; Bray Patrick-Lake; Sally Morton; Michael Pencina; Gowri Raman; Joseph S Ross; Harry P Selker; Ravi Varadhan; Andrew Vickers; John B Wong; Ewout W Steyerberg
Journal:  Ann Intern Med       Date:  2019-11-12       Impact factor: 25.391

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