A pretest for using logrank or Wilcoxon in the two-sample problem

In a two-sample location-scale model with censored data, the logrank test is asymptotically efficient when the error distribution is extreme minimum value. On the other hand, the Wilcoxon test is asymptotically efficient when the error distribution is logistic. We propose a pretest for choosing between logrank and Wilcoxon by determining if the error distribution is closer to extreme minimum value or logistic. This adaptive test is compared with the logrank and Wilcoxon tests through simulation.     

Privacy-preserving models for comparing survival curves using the logrank test

The incorporation of electronic health care in medical institutions will benefit and thus further boost the collaborations in medical research among clinics and research institutions. However, privacy regulations and security concerns make such collaborations very restricted. In this paper, we propose privacy preserving models for survival curves comparison based on logrank test, in order to perform better survival analysis through the collaboration of multiple medical institutions and protect the data privacy. We distinguish two collaboration scenarios and for each scenario we present a privacy preserving model for logrank test. We conduct experiments on the real medical data to evaluate the effectiveness of our proposed models.     

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

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.     

Proportional hazards modelling versus two-sample tests: A case study

The aim of this paper is to illustrate primarily the suitability and secondly the ability of both PHM and ‘two-sample’ tests in answering problems in the reliability field. The findings stress the fact that ‘two-sample’ tests should be preferred for comparison purposes and PHM for modelling the behaviour of the equipment in the presence of various factors.     

A SAS macro for a clustered logrank test

The clustered logrank test is a nonparametric method of significance testing for correlated survival data. Examples of its application include cluster randomized trials where groups of patients rather than individuals are randomized to either a treatment or a control intervention. We describe a SAS macro that implements the 2-sample clustered logrank test for data where the entire cluster is randomized to the same treatment group. We discuss the theory and applications behind this test as well as details of the SAS code.