Renewable risk assessment of heterogeneous streaming time-to-event cohorts.
Stat Med
; 43(20): 3761-3777, 2024 Sep 10.
Article
en En
| MEDLINE
| ID: mdl-38897797
ABSTRACT
The analysis of streaming time-to-event cohorts has garnered significant research attention. Most existing methods require observed cohorts from a study sequence to be independent and identically sampled from a common model. This assumption may be easily violated in practice. Our methodology operates within the framework of online data updating, where risk estimates for each cohort of interest are continuously refreshed using the latest observations and historical summary statistics. At each streaming stage, we introduce parameters to quantify the potential discrepancy between batch-specific effects from adjacent cohorts. We then employ penalized estimation techniques to identify nonzero discrepancy parameters, allowing us to adaptively adjust risk estimates based on current data and historical trends. We illustrate our proposed method through extensive empirical simulations and a lung cancer data analysis.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Neoplasias Pulmonares
Límite:
Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article