Cargando…
Validating online approaches for rare disease research using latent class mixture modeling
BACKGROUND: Rare disease patients are geographically dispersed, posing challenges to research. Some researchers have partnered with patient organizations and used web-based approaches to overcome geographic recruitment barriers. Critics of such methods claim that samples are homogenous and do not re...
Autores principales: | Dwyer, Andrew A., Zeng, Ziwei, Lee, Christopher S. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108361/ https://www.ncbi.nlm.nih.gov/pubmed/33971926 http://dx.doi.org/10.1186/s13023-021-01827-z |
Ejemplares similares
-
Latent Class Trajectory and Growth Mixture Models in the Study of Physical Resilience
por: Pieper, Carl, et al.
Publicado: (2020) -
Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research
por: Gilroy, Shawn P., et al.
Publicado: (2022) -
Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes
por: Kim, Eun Sook, et al.
Publicado: (2017) -
Automated Bot Detection Using Bayesian Latent Class Models in Online Surveys
por: Roman, Zachary Joseph, et al.
Publicado: (2022) -
A Bayesian latent class mixture model with censoring for correlation analysis in antimicrobial resistance across populations
por: Zhang, Min, et al.
Publicado: (2021)