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Missing link survival analysis with applications to available pandemic data

Gámiz Pérez, M. L., Mammen, E., Martinez-Miranda, M. D. & Nielsen, J. P. ORCID: 0000-0002-2798-0817 (2022). Missing link survival analysis with applications to available pandemic data. Computational Statistics & Data Analysis, 169, 107405. doi: 10.1016/j.csda.2021.107405

Abstract

It is shown how to overcome a new missing data problem in survival analysis. Iterative nonparametric techniques are utilized and the missing data information is both estimated and used for further estimation in each iterative step. Theory is developed and a good finite sample performance is illustrated by simulations. The main motivation is an application to French data on the temporal development of the number of hospitalized Covid-19 patients.

Publication Type: Article
Additional Information: © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Double one-sided cross-validation, Hazard, Local linear estimation, Missing data
Subjects: B Philosophy. Psychology. Religion > BF Psychology
H Social Sciences > HA Statistics
H Social Sciences > HN Social history and conditions. Social problems. Social reform
Q Science > QA Mathematics
Q Science > QR Microbiology > QR180 Immunology
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Departments: School of Health & Psychological Sciences > Psychology
[img] Text - Accepted Version
This document is not freely accessible until 21 December 2022 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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