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[null,null,"Titolo",null,"Applied missing data analysis in the health sciences / / Xiao-Hua Zhou [and three others]"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, New Jersey : , : John Wiley & Sons, , 2014"]
[{"$nulls":4},"2014"]
[null,null,"ISBN",null,"1-118-57363-3"]
[null,null,"Descrizione fisica",null,"1 online resource (256 pages)"]
[null,null,"Collana",null,"Wiley Series in Statistics in Practice"]
[null,null,"Disciplina",null,"610.711"]
[null,null,"Soggetti",null,"Medical sciences - Study and teaching"]
[{"$nulls":4},"Medicine - Research"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Missing data concepts and motivating examples -- Overview of methods for dealing with missing data -- Design considerations in the presence of missing data -- Crosssectional data methods -- Longitudinal data methods -- Survival analysis under ignorable missingness -- Nonignorable missingness."]
[null,null,null,"Sommario/riassunto","A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine."]