Missing link survival analysis with applications to available pandemic data

María Luz Gámiz; Enno Mammen; María Dolores Martínez-Miranda; Jens Perch Nielsen

doi:10.1016/j.csda.2021.107405

Missing link survival analysis with applications to available pandemic data

Comput Stat Data Anal. 2022 May:169:107405. doi: 10.1016/j.csda.2021.107405. Epub 2021 Dec 13.

Authors

María Luz Gámiz¹, Enno Mammen², María Dolores Martínez-Miranda¹, Jens Perch Nielsen³

Affiliations

¹ Department of Statistics and Operations Research, University of Granada, Spain.
² Institute of Applied Mathematics, Heidelberg University, Germany.
³ Bayes Business School, City, University of London, UK.

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.

Keywords: Double one-sided cross-validation; Hazard; Local linear estimation; Missing data.