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

Giampiero Marra, Alessio Farcomeni and Rosalba Radice

Computational Statistics & Data Analysis, 2021, vol. 155, issue C

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.

Keywords: Additive predictor; Link function; Mixed censoring; Penalised log-likelihood; Regression splines; Survival data (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:155:y:2021:i:c:s0167947320301833

DOI: 10.1016/j.csda.2020.107092

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