• Produktbild: Preventing and Treating Missing Data in Longitudinal Clinical Trials
  • Produktbild: Preventing and Treating Missing Data in Longitudinal Clinical Trials

Preventing and Treating Missing Data in Longitudinal Clinical Trials A Practical Guide

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

14.03.2013

Verlag

Cambridge Academic

Seitenzahl

184

Maße (L/B/H)

25/17,5/1,5 cm

Gewicht

520 g

Sprache

Englisch

ISBN

978-1-107-03138-8

Beschreibung

Zitat

'... this monograph is good value, and I recommend all those involved in the design, conduct or analysis of trials to peruse a copy. Non-statisticians will inevitably be frustrated at times, but if this monograph fosters improved discussion and understanding of the issues raised by missing data in study teams - and how they might be addressed - it will have done its work. In his choice of audience Mallinckrodt set himself a high bar ... it has ... been cleared. In addition, [his] wry turn of phrase was an unexpected pleasure. You'll miss this if you don't buy it!' James R. Carpenter, Journal of Biopharmaceutical Statistics

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

14.03.2013

Verlag

Cambridge Academic

Seitenzahl

184

Maße (L/B/H)

25/17,5/1,5 cm

Gewicht

520 g

Sprache

Englisch

ISBN

978-1-107-03138-8

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: Libri GmbH

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  • Produktbild: Preventing and Treating Missing Data in Longitudinal Clinical Trials
  • Produktbild: Preventing and Treating Missing Data in Longitudinal Clinical Trials
  • Part I. Background and Setting: 1. Why missing data matter; 2. Missing data mechanisms; 3. Estimands; Part II. Preventing Missing Data: 4. Trial design considerations; 5. Trial conduct considerations; Part III. Analytic Considerations: 6. Methods of estimation; 7. Models and modeling considerations; 8. Methods of dealing with missing data; Part IV. Analyses and the Analytic Road Map: 9. Analyses of incomplete data; 10. MNAR analyses; 11. Choosing primary estimands and analyses; 12. The analytic road map; 13. Analyzing incomplete categorical data; 14. Example; 15. Putting principles into practice.