An extended proportional hazards model for interval-censored data subject to instantaneous failures View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-02-23

AUTHORS

Prabhashi W. Withana Gamage, Monica Chaudari, Christopher S. McMahan, Edwin H. Kim, Michael R. Kosorok

ABSTRACT

The proportional hazards (PH) model is arguably one of the most popular models used to analyze time to event data arising from clinical trials and longitudinal studies. In many such studies, the event time is not directly observed but is known relative to periodic examination times; i.e., practitioners observe either current status or interval-censored data. The analysis of data of this structure is often fraught with many difficulties since the event time of interest is unobserved. Further exacerbating this issue, in some such studies the observed data also consists of instantaneous failures; i.e., the event times for several study units coincide exactly with the time at which the study begins. In light of these difficulties, this work focuses on developing a mixture model, under the PH assumptions, which can be used to analyze interval-censored data subject to instantaneous failures. To allow for modeling flexibility, two methods of estimating the unknown cumulative baseline hazard function are proposed; a fully parametric and a monotone spline representation are considered. Through a novel data augmentation procedure involving latent Poisson random variables, an expectation–maximization (EM) algorithm is developed to complete model fitting. The resulting EM algorithm is easy to implement and is computationally efficient. Moreover, through extensive simulation studies the proposed approach is shown to provide both reliable estimation and inference. The motivation for this work arises from a randomized clinical trial aimed at assessing the effectiveness of a new peanut allergen treatment in attaining sustained unresponsiveness in children. More... »

PAGES

158-182

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10985-019-09467-z

DOI

http://dx.doi.org/10.1007/s10985-019-09467-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112305478

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30796598


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