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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Majestic Books, Hounslow, Reino Unido
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Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2020
ISBN 10: 0367736586 ISBN 13: 9780367736583
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
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Añadir al carritoPaperback. Condición: New. Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.The book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 336 pages. 9.21x6.14x0.71 inches. In Stock.
Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2020
ISBN 10: 0367736586 ISBN 13: 9780367736583
Librería: Rarewaves.com UK, London, Reino Unido
EUR 78,88
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Añadir al carritoPaperback. Condición: New. Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.The book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan.
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 78,01
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Añadir al carritoPAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Missing data in longitudinal clinical trials has justifiably been the target of considerable research. However, missing data is just one of the many considerations in the analysis of longitudinal data, and focus on the data we don't have should not distract from focus on the data we do have. The statistical theory relevant to analyses of longitu.