An Expectation-Maximization Algorithm for Including Oncological COVID-19 Deaths in Survival Analysis.
COVID-19
em algorithm
extended greenwood’s formula
informative censoring
kaplan-meier estimator
mean-imputation
survival analysis
Journal
Current oncology (Toronto, Ont.)
ISSN: 1718-7729
Titre abrégé: Curr Oncol
Pays: Switzerland
ID NLM: 9502503
Informations de publication
Date de publication:
08 02 2023
08 02 2023
Historique:
received:
26
12
2022
revised:
31
01
2023
accepted:
03
02
2023
entrez:
24
2
2023
pubmed:
25
2
2023
medline:
3
3
2023
Statut:
epublish
Résumé
We address the problem of how COVID-19 deaths observed in an oncology clinical trial can be consistently taken into account in typical survival estimates. We refer to oncological patients since there is empirical evidence of strong correlation between COVID-19 and cancer deaths, which implies that COVID-19 deaths cannot be treated simply as non-informative censoring, a property usually required by the classical survival estimators. We consider the problem in the framework of the widely used Kaplan-Meier (KM) estimator. Through a counterfactual approach, an algorithmic method is developed allowing to include COVID-19 deaths in the observed data by mean-imputation. The procedure can be seen in the class of the
Identifiants
pubmed: 36826124
pii: curroncol30020163
doi: 10.3390/curroncol30020163
pmc: PMC9955008
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2105-2126Références
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