Literature DB >> 24567661

Sample size determination for paired right-censored data based on the difference of Kaplan-Meier estimates.

Pei-Fang Su1, Chung-I Li2, Yu Shyr3.   

Abstract

Sample size determination is essential to planning clinical trials. Jung (2008) established a sample size calculation formula for paired right-censored data based on the logrank test, which has been well-studied for comparing independent survival outcomes. An alternative to rank-based methods for independent right-censored data, advocated by Pepe and Fleming (1989), tests for differences between integrated weighted Kaplan-Meier estimates and is more sensitive to the magnitude of difference in survival times between groups. In this paper, we employ the concept of the Pepe-Fleming method to determine an adequate sample size by calculating differences between Kaplan-Meier estimators considering pair-wise correlation. We specify a positive stable frailty model for the joint distribution of paired survival times. We evaluate the performance of the proposed method by simulation studies and investigate the impacts of the accrual times, follow-up times, loss to follow-up rate, and sensitivity of power under misspecification of the model. The results show that ignoring the pair-wise correlation results in overestimating the required sample size. Furthermore, the proposed method is applied to two real-world studies, and the R code for sample size calculation is made available to users.

Entities:  

Keywords:  Kaplan-Meier statistic; Logrank test; hazard function; paired observation; positive stable frailty model

Year:  2014        PMID: 24567661      PMCID: PMC3931470          DOI: 10.1016/j.csda.2013.12.006

Source DB:  PubMed          Journal:  Comput Stat Data Anal        ISSN: 0167-9473            Impact factor:   1.681


  12 in total

1.  Nonparametric rank-based methods for group sequential monitoring of paired censored survival data.

Authors:  S Murray
Journal:  Biometrics       Date:  2000-12       Impact factor: 2.571

2.  Rank tests for matched survival data.

Authors:  S H Jung
Journal:  Lifetime Data Anal       Date:  1999       Impact factor: 1.588

3.  Using weighted Kaplan-Meier statistics in nonparametric comparisons of paired censored survival outcomes.

Authors:  S Murray
Journal:  Biometrics       Date:  2001-06       Impact factor: 2.571

4.  A GENERALIZED WILCOXON TEST FOR COMPARING ARBITRARILY SINGLY-CENSORED SAMPLES.

Authors:  E A GEHAN
Journal:  Biometrika       Date:  1965-06       Impact factor: 2.445

5.  The ETDRS and Diabetes 2000.

Authors:  A Patz; R E Smith
Journal:  Ophthalmology       Date:  1991-05       Impact factor: 12.079

6.  Simultaneous group sequential analysis of rank-based and weighted Kaplan-Meier tests for paired censored survival data.

Authors:  Adin-Cristian Andrei; Susan Murray
Journal:  Biometrics       Date:  2005-09       Impact factor: 2.571

7.  Sample size calculation for the weighted rank statistics with paired survival data.

Authors:  Sin-Ho Jung
Journal:  Stat Med       Date:  2008-07-30       Impact factor: 2.373

8.  The simultaneous use of weighted logrank and weighted Kaplan-Meier statistics with clustered right-censored data.

Authors:  Yunchan Chi; Pei-Fang Su
Journal:  Stat Med       Date:  2010-01-15       Impact factor: 2.373

9.  Weighted Kaplan-Meier statistics: a class of distance tests for censored survival data.

Authors:  M S Pepe; T R Fleming
Journal:  Biometrics       Date:  1989-06       Impact factor: 2.571

10.  Analyzing survival curves at a fixed point in time for paired and clustered right-censored data.

Authors:  Pei-Fang Su; Yunchan Chi; Chun-Yi Lee; Yu Shyr; Yi-De Liao
Journal:  Comput Stat Data Anal       Date:  2010-10-21       Impact factor: 1.681

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