Cargando…
Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review
Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them...
Autores principales: | Masconi, Katya L, Matsha, Tandi E, Echouffo-Tcheugui, Justin B, Erasmus, Rajiv T, Kengne, Andre P |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380106/ https://www.ncbi.nlm.nih.gov/pubmed/25829972 http://dx.doi.org/10.1186/s13167-015-0028-0 |
Ejemplares similares
-
Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa
por: Masconi, Katya L., et al.
Publicado: (2015) -
Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans
por: Mogueo, Amelie, et al.
Publicado: (2015) -
Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa
por: Masconi, Katya, et al.
Publicado: (2015) -
Effect of model updating strategies on the performance of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa
por: Masconi, Katya L., et al.
Publicado: (2019) -
APOL1 genetic variants, chronic kidney diseases and hypertension in mixed ancestry South Africans
por: Matsha, Tandi E, et al.
Publicado: (2015)