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Link-based survival additive models under mixed censoring to assess risks of hospital-acquired infections

Marra, G; Farcomeni, A; Radice, R; (2021) Link-based survival additive models under mixed censoring to assess risks of hospital-acquired infections. Computational Statistics & Data Analysis , 155 , Article 107092. 10.1016/j.csda.2020.107092.

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Abstract

The majority of methods available to model survival data only deal with right censoring. However, there are many applications where left, right and/or interval censoring simultaneously occur. A methodology that is capable of handling all types of censoring as well as flexibly estimating several types of covariate effects is presented. The baseline hazard is modelled through monotonic P-splines. The model’s parameters are estimated using an efficient and stable penalised likelihood algorithm. The proposed framework is evaluated in simulation, and illustrated using an original data example on time to first hospital infection or in-hospital death in cirrhotic patients. A peak of risk in the first week since hospitalisation is identified, together with a non-linear effect of Model for End-Stage Liver Disease (MELD) score. The GJRM R package, with an implementation of our approach, is freely available on CRAN.

Type: Article
Title: Link-based survival additive models under mixed censoring to assess risks of hospital-acquired infections
DOI: 10.1016/j.csda.2020.107092
Publisher version: https://doi.org/10.1016/j.csda.2020.107092
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Additive predictor, Link function, Mixed censoring, Penalised log-likelihood, Regression splines, Survival data
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10114131
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