Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review ; volume:24 ; number:1 ; day:28 ; month:8 ; year:2024 ; pages:1-13 ; date:12.2024
BMC medical research methodology ; 24, Heft 1 (28.8.2024), 1-13, 12.2024
- Creator
-
Afkanpour, Marziyeh
Hosseinzadeh, Elham
Tabesh, Hamed
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s12874-024-02310-6
- URN
-
urn:nbn:de:101:1-2411132101127.518964277514
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:23 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Afkanpour, Marziyeh
- Hosseinzadeh, Elham
- Tabesh, Hamed
- SpringerLink (Online service)
Other Objects (12)
Imputation of missing values in lipidomic datasets
A data-driven missing value imputation approach for longitudinal datasets
Comparison of imputation methods for handling missing categorical data with univariate pattern
Multiple imputation for handling missing outcome data when estimating the relative risk
Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
The impact of imputation quality on machine learning classifiers for datasets with missing values
Missing Data, Imputation, and Endogeneity
Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
Missing value imputation for epistatic MAPs
Imputation methods for mixed datasets in bioarchaeology
Fast accurate missing SNP genotype local imputation
SICE: an improved missing data imputation technique
Imputation of missing values in lipidomic datasets
A data-driven missing value imputation approach for longitudinal datasets
Comparison of imputation methods for handling missing categorical data with univariate pattern
Multiple imputation for handling missing outcome data when estimating the relative risk
Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
The impact of imputation quality on machine learning classifiers for datasets with missing values
Missing Data, Imputation, and Endogeneity
Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
Missing value imputation for epistatic MAPs
Imputation methods for mixed datasets in bioarchaeology
Fast accurate missing SNP genotype local imputation
SICE: an improved missing data imputation technique
Imputation of missing values in lipidomic datasets
A data-driven missing value imputation approach for longitudinal datasets
Comparison of imputation methods for handling missing categorical data with univariate pattern
Multiple imputation for handling missing outcome data when estimating the relative risk
Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
The impact of imputation quality on machine learning classifiers for datasets with missing values
Missing Data, Imputation, and Endogeneity
Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
Missing value imputation for epistatic MAPs
Imputation methods for mixed datasets in bioarchaeology
Fast accurate missing SNP genotype local imputation