Literature DB >> 29282764

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

Qing Yang1, Wing K Fung2, Gang Li3.   

Abstract

This article considers sample size determination for jointly testing a cause-specific hazard and the all-cause hazard for competing risks data. The cause-specific hazard and the all-cause hazard jointly characterize important study end points such as the disease-specific survival and overall survival, which are commonly used as coprimary end points in clinical trials. Specifically, we derive sample size calculation methods for 2-group comparisons based on an asymptotic chi-square joint test and a maximum joint test of the aforementioned quantities, taking into account censoring due to lost to follow-up as well as staggered entry and administrative censoring. We illustrate the application of the proposed methods using the Die Deutsche Diabetes Dialyse Studies clinical trial. An R package "powerCompRisk" has been developed and made available at the CRAN R library.
Copyright © 2017 John Wiley & Sons, Ltd.

Entities:  

Keywords:  2-sample test; competing risks; joint test; power analysis; sample size

Mesh:

Year:  2017        PMID: 29282764      PMCID: PMC6148356          DOI: 10.1002/sim.7590

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.497


  19 in total

1.  Sample size considerations for the evaluation of prognostic factors in survival analysis.

Authors:  C Schmoor; W Sauerbrei; M Schumacher
Journal:  Stat Med       Date:  2000-02-29       Impact factor: 2.373

2.  Sample sizes for proportional hazards survival studies with arbitrary patient entry and loss to follow-up distributions.

Authors:  N A Yateman; A M Skene
Journal:  Stat Med       Date:  1992-06-15       Impact factor: 2.373

3.  Sample size formula for proportional hazards modelling of competing risks.

Authors:  Aurélien Latouche; Raphaël Porcher; Sylvie Chevret
Journal:  Stat Med       Date:  2004-11-15       Impact factor: 2.373

Review 4.  A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions.

Authors:  Aurelien Latouche; Arthur Allignol; Jan Beyersmann; Myriam Labopin; Jason P Fine
Journal:  J Clin Epidemiol       Date:  2013-02-14       Impact factor: 6.437

5.  Sample size determination in clinical trials with time-dependent rates of losses and noncompliance.

Authors:  E Lakatos
Journal:  Control Clin Trials       Date:  1986-09

6.  Introduction to sample size determination and power analysis for clinical trials.

Authors:  J M Lachin
Journal:  Control Clin Trials       Date:  1981-06

7.  Apolipoprotein B, fibrinogen, HDL cholesterol, and apolipoprotein(a) phenotypes predict coronary artery disease in hemodialysis patients.

Authors:  M Koch; B Kutkuhn; E Trenkwalder; D Bach; B Grabensee; H Dieplinger; F Kronenberg
Journal:  J Am Soc Nephrol       Date:  1997-12       Impact factor: 10.121

8.  Sample sizes for clinical trials with time-to-event endpoints and competing risks.

Authors:  Gabi Schulgen; Manfred Olschewski; Vera Krane; Christoph Wanner; Günther Ruf; Martin Schumacher
Journal:  Contemp Clin Trials       Date:  2005-04-26       Impact factor: 2.226

9.  Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification.

Authors:  J M Lachin; M A Foulkes
Journal:  Biometrics       Date:  1986-09       Impact factor: 2.571

10.  Evaluation of sample size and power for multi-arm survival trials allowing for non-uniform accrual, non-proportional hazards, loss to follow-up and cross-over.

Authors:  F M-S Barthel; A Babiker; P Royston; M K B Parmar
Journal:  Stat Med       Date:  2006-08-15       Impact factor: 2.373

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