Literature DB >> 23063467

Competing event risk stratification may improve the design and efficiency of clinical trials: secondary analysis of SWOG 8794.

Kaveh Zakeri1, Brent S Rose, Sachin Gulaya, Anthony V D'Amico, Loren K Mell.   

Abstract

BACKGROUND: Composite endpoints can be problematic in the presence of competing risks when a treatment does not affect events comprising the endpoint equally.
METHODS: We conducted secondary analysis of SWOG 8794 trial of adjuvant radiation therapy (RT) for high-risk post-operative prostate cancer. The primary outcome was metastasis-free survival (MFS), defined as time to first occurrence of metastasis or death from any cause (competing mortality (CM)). We developed separate risk scores for time to metastasis and CM using competing risks regression. We estimated treatment effects using Cox models adjusted for risk scores and identified an enriched subgroup of 75 patients at high risk of metastasis and low risk of CM.
RESULTS: The mean CM risk score was significantly lower in the RT arm vs. control arm (p=0.001). The effect of RT on MFS (HR 0.70; 95% CI, 0.53-0.92; p=0.010) was attenuated when controlling for metastasis and CM risk (HR 0.76; 95% CI, 0.58-1.00; p=0.049), and the effect of RT on overall survival (HR 0.73; 95% CI, 0.55-0.96; p=0.02) was no longer significant when controlling for metastasis and CM risk (HR 0.80; 95% CI, 0.60-1.06; p=0.12). Compared to the whole sample, the enriched subgroup had the same 10-year incidence of MFS (40%; 95% CI, 22-57%), but a higher incidence of metastasis (30% (95% CI, 15-47%) vs. 20% (95% CI, 15-26%)). A randomized trial in the subgroup would have achieved 80% power with 56% less patients (313 vs. 709, respectively).
CONCLUSION: Stratification on competing event risk may improve the efficiency of clinical trials.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 23063467      PMCID: PMC3525784          DOI: 10.1016/j.cct.2012.09.008

Source DB:  PubMed          Journal:  Contemp Clin Trials        ISSN: 1551-7144            Impact factor:   2.226


  15 in total

1.  Dealing with competing risks: testing covariates and calculating sample size.

Authors:  Melania Pintilie
Journal:  Stat Med       Date:  2002-11-30       Impact factor: 2.373

2.  Composite outcomes in randomized trials: greater precision but with greater uncertainty?

Authors:  Nick Freemantle; Melanie Calvert; John Wood; Joanne Eastaugh; Carl Griffin
Journal:  JAMA       Date:  2003-05-21       Impact factor: 56.272

3.  Pitfalls of using composite primary end points in the presence of competing risks.

Authors:  Loren K Mell; Jong-Hyeon Jeong
Journal:  J Clin Oncol       Date:  2010-08-16       Impact factor: 44.544

4.  The use and interpretation of competing risks regression models.

Authors:  James J Dignam; Qiang Zhang; Masha Kocherginsky
Journal:  Clin Cancer Res       Date:  2012-01-26       Impact factor: 12.531

5.  More on cetuximab in head and neck cancer.

Authors:  Loren K Mell; Ralph R Weichselbaum
Journal:  N Engl J Med       Date:  2007-11-22       Impact factor: 91.245

6.  The analysis of failure times in the presence of competing risks.

Authors:  R L Prentice; J D Kalbfleisch; A V Peterson; N Flournoy; V T Farewell; N E Breslow
Journal:  Biometrics       Date:  1978-12       Impact factor: 2.571

7.  Population-based study of competing mortality in head and neck cancer.

Authors:  Brent S Rose; Jong-Hyeon Jeong; Sameer K Nath; Sharon M Lu; Loren K Mell
Journal:  J Clin Oncol       Date:  2011-08-15       Impact factor: 44.544

Review 8.  Endpoints in adjuvant treatment trials: a systematic review of the literature in colon cancer and proposed definitions for future trials.

Authors:  Cornelis J A Punt; Marc Buyse; Claus-Henning Köhne; Peter Hohenberger; Roberto Labianca; Hans J Schmoll; Lars Påhlman; Alberto Sobrero; Jean-Yves Douillard
Journal:  J Natl Cancer Inst       Date:  2007-06-27       Impact factor: 13.506

9.  Choice and interpretation of statistical tests used when competing risks are present.

Authors:  James J Dignam; Maria N Kocherginsky
Journal:  J Clin Oncol       Date:  2008-08-20       Impact factor: 44.544

10.  Adjuvant radiotherapy for pathological T3N0M0 prostate cancer significantly reduces risk of metastases and improves survival: long-term followup of a randomized clinical trial.

Authors:  Ian M Thompson; Catherine M Tangen; Jorge Paradelo; M Scott Lucia; Gary Miller; Dean Troyer; Edward Messing; Jeffrey Forman; Joseph Chin; Gregory Swanson; Edith Canby-Hagino; E David Crawford
Journal:  J Urol       Date:  2009-01-23       Impact factor: 7.450

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  4 in total

1.  Nomogram to Predict the Benefit of Intensive Treatment for Locoregionally Advanced Head and Neck Cancer.

Authors:  Loren K Mell; Hanjie Shen; Phuc Felix Nguyen-Tân; David I Rosenthal; Kaveh Zakeri; Lucas K Vitzthum; Steven J Frank; Peter B Schiff; Andy M Trotti; James A Bonner; Christopher U Jones; Sue S Yom; Wade L Thorstad; Stuart J Wong; George Shenouda; John A Ridge; Qiang E Zhang; Quynh-Thu Le
Journal:  Clin Cancer Res       Date:  2019-08-16       Impact factor: 12.531

2.  [Long-term results of adjuvant versus early salvage radiation therapy in pT3N0 prostate cancer after radical prostatectomy].

Authors:  R M Hermann; H Christiansen
Journal:  Strahlenther Onkol       Date:  2018-02       Impact factor: 3.621

3.  Validated competing event model for the stage I-II endometrial cancer population.

Authors:  Ruben Carmona; Sachin Gulaya; James D Murphy; Brent S Rose; John Wu; Sonal Noticewala; Michael T McHale; Catheryn M Yashar; Florin Vaida; Loren K Mell
Journal:  Int J Radiat Oncol Biol Phys       Date:  2014-07-15       Impact factor: 7.038

Review 4.  Specificity of Genetic Biomarker Studies in Cancer Research: A Systematic Review.

Authors:  Garrett Green; Ruben Carmona; Kaveh Zakeri; Chih-Han Lee; Saif Borgan; Zaid Marhoon; Andrew Sharabi; Loren K Mell
Journal:  PLoS One       Date:  2016-07-06       Impact factor: 3.240

  4 in total

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