Generalized log‐rank test for mixed interval‐censored failure time data

Abstract

This paper considers the problem of non-parametric treatment comparisons when mixed interval-censored failure time data are available, which often occurs in clinical trials and epidemiological studies. By mixed interval-censored data, we mean that the survival time of interest is observed to belong to an interval or to be right-censored. For the problem, we generalize the most commonly used log-rank test for right-censored survival data. Numerical studies are conducted and reported to evaluate and compare the proposed test with the existing method, which indicate that the presented method works well. We apply the method to a data set arising from an AIDS cohort study, that motivated the study.

Achieving and maintaining asthma control in inner-city children

“…This produced a generalized log-rank test II. 58,59 The generalized log-rank test II results confirmed a statistically significant difference between the 2 adherence groups and race groups when examined within each baseline severity group (compared with unadjusted score test statistic produced by PHREG procedure where midpoint imputation applied).…”

Addressing post-transplant summer water stress in Pinus pinea and Quercus ilex seedlings

“…The generalized log-rank test results confirmed a statistically significant difference between treatments, which was more significant in stone pine (p<0.001, for both tests) than in holm oak (p<0.01 for the test of Zhao & Sun (2004), and p<0.01 for the test of Sun et al (2005)). Results show that mulching was not a significant predictor of survival once irrigation was applied.…”

“…In a first step, we estimated and represented the survival distribution of stone pine and holm oak seedlings in each treatment using a nonparametric maximum likelihood estimator (NPMLE), which is computed through the algorithm developed by Wellner & Zhan (1997). In a second step, we used two generalized log-rank tests developed for interval-censored failure time data to test the hypothesis of whether there was a significant treatment effect or not (Zhao & Sun 2004, Sun et al 2005. These two firsts steps of the survival analysis were implemented in SAS using the macros developed by So & Johnston (2010).…”

On assessing the presence of evaluation‐time bias in progression‐free survival in randomized trials

“…Alternative methods based on interval-censored data can also be considered for analysis. See Finkelstein [7] and more recently Zhao and Sun [8] for discussion of these approaches.…”