Cargando…
Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials
Time series data collected in clinical trials can have varying degrees of missingness, adding challenges during statistical analyses. An additional layer of complexity is introduced for missing data in randomized controlled trials (RCT), where researchers must remain blinded between intervention and...
Autores principales: | Kim, Yohan, Kelly, Scott, Krishnan, Deepu, Falletta, Jay, Wilmot, Kerryn |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835538/ https://www.ncbi.nlm.nih.gov/pubmed/35162331 http://dx.doi.org/10.3390/ijerph19031307 |
Ejemplares similares
-
Imputation strategies for missing binary outcomes in cluster randomized trials
por: Ma, Jinhui, et al.
Publicado: (2011) -
Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data
por: Ampong, Isaac, et al.
Publicado: (2022) -
Data Driven Estimation of Imputation Error—A Strategy for Imputation with a Reject Option
por: Bak, Nikolaj, et al.
Publicado: (2016) -
Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
por: Liao, Serena G, et al.
Publicado: (2014) -
Should multiple imputation be the method of choice for handling missing data in randomized trials?
por: Sullivan, Thomas R, et al.
Publicado: (2016)