Every few days, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.
The article featured today is from Statistics in Medicine, with the full article now available to read in issue 39:4 here.
, . Association measures for clustered competing risks. Statistics in Medicine. 2020; 39: 409– 423. https://doi.org/10.1002/sim.8413
In clinical studies, failure time data with a fraction of cure is common. The data corresponds to a portion of subjects who may never experience the event of interest and can be considered cured from the event. In the study of appendectomy, for example, some subjects may survive without any appendectomy and can be considered cured from appendectomy. In the smoking cessation study, a smoker may successfully give up smoking and never smoke again. The analysis for such data should take the fraction of cure into account. Furthermore, the data may exist in clusters, such as twins in the appendectomy study and geographical regions in the smoking cessation study. The analysis should also take into account the dependence between responses in the same cluster.
This article aims to propose a novel statistical method for such data type. Using our approach, researchers can achieve three goals: 1) explore risk factors for the time to event, 2) estimate the fraction of cure, and 3) assess the strength of association between individuals in the same cluster. The new methodology is illustrated with applications to two clinical studies.