Preventing and treating missing data in longitudinal clinical trials: a practical guide
Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book f...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2013
|
| Schriftenreihe: | Practical guides to biostatistics and epidemiology
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| Schlagworte: | |
| Zusammenfassung: | Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | xviii, 165 pages |
| ISBN: | 9781139381666 9781107031388 9781107679153 |
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| 100 | 1 | |a Mallinckrodt, Craig H. |d 1958- |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Preventing and treating missing data in longitudinal clinical trials |b a practical guide |c Craig H. Mallinckrodt |
| 246 | 1 | 3 | |a Preventing & Treating Missing Data in Longitudinal Clinical Trials |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2013 | |
| 300 | |a xviii, 165 pages | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Practical guides to biostatistics and epidemiology | |
| 500 | |a Title from publisher's bibliographic system (viewed on 05 Oct 2015) | ||
| 505 | 8 | |a Machine generated contents note: 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 | |
| 520 | |a Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Clinical trials / Longitudinal studies | |
| 650 | 4 | |a Medical sciences / Statistical methods | |
| 650 | 4 | |a Regression analysis / Data processing | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-107-67915-3 |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030106725 | ||
Datensatz im Suchindex
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| author | Mallinckrodt, Craig H. 1958- |
| author_facet | Mallinckrodt, Craig H. 1958- |
| author_role | aut |
| author_sort | Mallinckrodt, Craig H. 1958- |
| author_variant | c h m ch chm |
| building | Verbundindex |
| bvnumber | BV044710174 |
| contents | Machine generated contents note: 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 |
| ctrlnum | (OCoLC)934897108 (DE-599)BVBBV044710174 |
| dewey-full | 610.72/4 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 610 - Medicine and health |
| dewey-raw | 610.72/4 |
| dewey-search | 610.72/4 |
| dewey-sort | 3610.72 14 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| format | Book |
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| id | DE-604.BV044710174 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:00:01Z |
| institution | BVB |
| isbn | 9781139381666 9781107031388 9781107679153 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030106725 |
| oclc_num | 934897108 |
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| owner_facet | DE-19 DE-BY-UBM |
| physical | xviii, 165 pages |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Cambridge University Press |
| record_format | marc |
| series2 | Practical guides to biostatistics and epidemiology |
| spelling | Mallinckrodt, Craig H. 1958- Verfasser aut Preventing and treating missing data in longitudinal clinical trials a practical guide Craig H. Mallinckrodt Preventing & Treating Missing Data in Longitudinal Clinical Trials Cambridge Cambridge University Press 2013 xviii, 165 pages txt rdacontent n rdamedia nc rdacarrier Practical guides to biostatistics and epidemiology Title from publisher's bibliographic system (viewed on 05 Oct 2015) Machine generated contents note: 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 Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset Datenverarbeitung Clinical trials / Longitudinal studies Medical sciences / Statistical methods Regression analysis / Data processing Erscheint auch als Online-Ausgabe 978-1-107-67915-3 |
| spellingShingle | Mallinckrodt, Craig H. 1958- Preventing and treating missing data in longitudinal clinical trials a practical guide Machine generated contents note: 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 Datenverarbeitung Clinical trials / Longitudinal studies Medical sciences / Statistical methods Regression analysis / Data processing |
| title | Preventing and treating missing data in longitudinal clinical trials a practical guide |
| title_alt | Preventing & Treating Missing Data in Longitudinal Clinical Trials |
| title_auth | Preventing and treating missing data in longitudinal clinical trials a practical guide |
| title_exact_search | Preventing and treating missing data in longitudinal clinical trials a practical guide |
| title_full | Preventing and treating missing data in longitudinal clinical trials a practical guide Craig H. Mallinckrodt |
| title_fullStr | Preventing and treating missing data in longitudinal clinical trials a practical guide Craig H. Mallinckrodt |
| title_full_unstemmed | Preventing and treating missing data in longitudinal clinical trials a practical guide Craig H. Mallinckrodt |
| title_short | Preventing and treating missing data in longitudinal clinical trials |
| title_sort | preventing and treating missing data in longitudinal clinical trials a practical guide |
| title_sub | a practical guide |
| topic | Datenverarbeitung Clinical trials / Longitudinal studies Medical sciences / Statistical methods Regression analysis / Data processing |
| topic_facet | Datenverarbeitung Clinical trials / Longitudinal studies Medical sciences / Statistical methods Regression analysis / Data processing |
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