Literature DB >> 15490425

Sample size formula for proportional hazards modelling of competing risks.

Aurélien Latouche1, Raphaël Porcher, Sylvie Chevret.   

Abstract

To test the effect of a therapeutic or prognostic factor on the occurrence of a particular cause of failure in the presence of other causes, the interest has shifted in some studies from the modelling of the cause-specific hazard to that of the subdistribution hazard. We present approximate sample size formulas for the proportional hazards modelling of competing risk subdistribution, considering either independent or correlated covariates. The validity of these approximate formulas is investigated through numerical simulations. Two illustrations are provided, a randomized clinical trial, and a prospective prognostic study. 2004 John Wiley & Sons, Ltd.

Mesh：

Substances：

Year:  2004        PMID： 15490425     DOI： 10.1002/sim.1915

Source DB:  PubMed          Journal:  Stat Med        ISSN： 0277-6715            Impact factor:   2.373


  25 in total

1.  A Simulation Based Evaluation of the Asymptotic Power Formulae for Cox Models in Small Sample Cases.

Authors:  Mehmet Kocak; Arzu Onar-Thomas
Journal:  Am Stat       Date:  2012       Impact factor: 8.710

2.  A group sequential test for treatment effect based on the Fine-Gray model.

Authors:  Michael J Martens; Brent R Logan
Journal:  Biometrics       Date:  2018-03-13       Impact factor: 2.571

3.  Risk factors for non-invasive and invasive local recurrence in patients with ductal carcinoma in situ.

Authors:  Laura C Collins; Ninah Achacoso; Reina Haque; Larissa Nekhlyudov; Suzanne W Fletcher; Charles P Quesenberry; Stuart J Schnitt; Laurel A Habel
Journal:  Breast Cancer Res Treat       Date:  2013-04-27       Impact factor: 4.872

Review 4.  Statistical Considerations for Analyses of Time-To-Event Endpoints in Oncology Clinical Trials: Illustrations with CAR-T Immunotherapy Studies.

Authors:  Yimei Li; Wei-Ting Hwang; Shannon L Maude; David T Teachey; Noelle V Frey; Regina M Myers; Allison Barz Leahy; Hongyan Liu; David L Porter; Stephan A Grupp; Pamela A Shaw
Journal:  Clin Cancer Res       Date:  2022-09-15       Impact factor: 13.801

5.  The use of group sequential designs with common competing risks tests.

Authors:  Brent R Logan; Mei-Jie Zhang
Journal:  Stat Med       Date:  2012-09-04       Impact factor: 2.373

6.  A unified approach to sample size and power determination for testing parameters in generalized linear and time-to-event regression models.

Authors:  Michael J Martens; Brent R Logan
Journal:  Stat Med       Date:  2020-11-18       Impact factor: 2.373

7.  Analysis and design of randomised clinical trials involving competing risks endpoints.

Authors:  Bee-Choo Tai; Joseph Wee; David Machin
Journal:  Trials       Date:  2011-05-19       Impact factor: 2.279

8.  Sample size determination for jointly testing a cause-specific hazard and the all-cause hazard in the presence of competing risks.

Authors:  Qing Yang; Wing K Fung; Gang Li
Journal:  Stat Med       Date:  2017-12-27       Impact factor: 2.497

9.  Outcome of ICU patients with Clostridium difficile infection.

Authors:  Jean-Ralph Zahar; Carole Schwebel; Christophe Adrie; Maité Garrouste-Orgeas; Adrien Français; Aurélien Vesin; Molière Nguile-Makao; Alexis Tabah; Kevin Laupland; Alban Le-Monnier; Jean-François Timsit
Journal:  Crit Care       Date:  2012-11-05       Impact factor: 9.097

10.  A procalcitonin-based algorithm to guide antibiotic therapy in secondary peritonitis following emergency surgery: a prospective study with propensity score matching analysis.

Authors:  Ting-Shuo Huang; Shie-Shian Huang; Yu-Chiau Shyu; Chun-Hui Lee; Shyh-Chuan Jwo; Pei-Jer Chen; Huang-Yang Chen
Journal:  PLoS One       Date:  2014-03-04       Impact factor: 3.240
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.