Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance

BACKGROUND: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real wor...

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Detalles Bibliográficos
Autores principales: Davis, Sharon E., Ssemaganda, Henry, Koola, Jejo D., Mao, Jialin, Westerman, Dax, Speroff, Theodore, Govindarajulu, Usha S., Ramsay, Craig R., Sedrakyan, Art, Ohno-Machado, Lucila, Resnic, Frederic S., Matheny, Michael E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088292/
https://www.ncbi.nlm.nih.gov/pubmed/37041457
http://dx.doi.org/10.1186/s12874-023-01913-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088292/
https://www.ncbi.nlm.nih.gov/pubmed/37041457
http://dx.doi.org/10.1186/s12874-023-01913-9

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