Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2288
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis ; volume:17 ; number:1 ; day:22 ; month:8 ; year:2017 ; pages:1-12 ; date:12.2017
BMC medical research methodology ; 17, Heft 1 (22.8.2017), 1-12, 12.2017
- Schlagwort
-
Krankenpflege
Medizinische Ausbildung
- Urheber
-
Eekhout, Iris
- Beteiligte Personen und Organisationen
-
Wiel, Mark A. van de
Heymans, Martijn W.
SpringerLink (Online service)
- DOI
-
10.1186/s12874-017-0404-7
- URN
-
urn:nbn:de:1111-201711042893
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:33 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Eekhout, Iris
- Wiel, Mark A. van de
- Heymans, Martijn W.
- SpringerLink (Online service)
Ähnliche Objekte (12)
High-cardinality categorical covariates in network regressions
Calibrated imputation for multivariate categorical data
Logistic regression with missing values in the covariates
Logistic regression with sparse common and distinctive covariates
Decision Trees for the Imputation of Categorical Data
Nonparametric regression and classification with functional, categorical, and mixed covariates
A nonparametric multiple imputation approach for missing categorical data
Comparison of imputation methods for univariate categorical longitudinal data
Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates
Generalizing treatment effects with incomplete covariates: Identifying assumptions and multiple imputation algorithms
Comparison of imputation methods for handling missing categorical data with univariate pattern
Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods
High-cardinality categorical covariates in network regressions
Calibrated imputation for multivariate categorical data
Logistic regression with missing values in the covariates
Logistic regression with sparse common and distinctive covariates
Decision Trees for the Imputation of Categorical Data
Nonparametric regression and classification with functional, categorical, and mixed covariates
A nonparametric multiple imputation approach for missing categorical data
Comparison of imputation methods for univariate categorical longitudinal data
Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates
Generalizing treatment effects with incomplete covariates: Identifying assumptions and multiple imputation algorithms
Comparison of imputation methods for handling missing categorical data with univariate pattern
Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods
High-cardinality categorical covariates in network regressions
Calibrated imputation for multivariate categorical data
Logistic regression with missing values in the covariates
Logistic regression with sparse common and distinctive covariates
Decision Trees for the Imputation of Categorical Data
Nonparametric regression and classification with functional, categorical, and mixed covariates
A nonparametric multiple imputation approach for missing categorical data
Comparison of imputation methods for univariate categorical longitudinal data
Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates
Generalizing treatment effects with incomplete covariates: Identifying assumptions and multiple imputation algorithms
Comparison of imputation methods for handling missing categorical data with univariate pattern