Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation.
COVID-19
Cox proportional hazards model
Sieve estimation
Survival analysis
Truncated data
Journal
Lifetime data analysis
ISSN: 1572-9249
Titre abrégé: Lifetime Data Anal
Pays: United States
ID NLM: 9516348
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
24
10
2021
accepted:
28
06
2022
pubmed:
14
7
2022
medline:
24
1
2023
entrez:
13
7
2022
Statut:
ppublish
Résumé
The incubation period is a key characteristic of an infectious disease. In the outbreak of a novel infectious disease, accurate evaluation of the incubation period distribution is critical for designing effective prevention and control measures . Estimation of the incubation period distribution based on limited information from retrospective inspection of infected cases is highly challenging due to censoring and truncation. In this paper, we consider a semiparametric regression model for the incubation period and propose a sieve maximum likelihood approach for estimation based on the symptom onset time, travel history, and basic demographics of reported cases. The approach properly accounts for the pandemic growth and selection bias in data collection. We also develop an efficient computation method and establish the asymptotic properties of the proposed estimators. We demonstrate the feasibility and advantages of the proposed methods through extensive simulation studies and provide an application to a dataset on the outbreak of COVID-19.
Identifiants
pubmed: 35831702
doi: 10.1007/s10985-022-09567-3
pii: 10.1007/s10985-022-09567-3
pmc: PMC9281361
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
87-114Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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