000			03096nam a2200409 i 4500
001			CR9781139381666
003			UkCbUP
005			20240916193725.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			141103s2013||||enk o ||1 0|eng|d
020			_a9781139381666 (ebook)
020			_z9781107031388 (hardback)
020			_z9781107679153 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aR853.C55 _bM3374 2013
082	0	0	_a610.72/4 _223
100	1		_aMallinckrodt, Craig H., _d1958- _eauthor.
245	1	0	_aPreventing and treating missing data in longitudinal clinical trials : _ba practical guide / _cCraig H. Mallinckrodt.
246	3		_aPreventing & Treating Missing Data in Longitudinal Clinical Trials
264		1	_aCambridge : _bCambridge University Press, _c2013.
300			_a1 online resource (xviii, 165 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aPractical guides to biostatistics and epidemiology
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_aMachine 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			_aRecent 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		0	_aClinical trials _vLongitudinal studies.
650		0	_aMedical sciences _xStatistical methods.
650		0	_aRegression analysis _xData processing.
776	0	8	_iPrint version: _z9781107031388
830		0	_aPractical guides to biostatistics and epidemiology.
856	4	0	_uhttps://doi.org/10.1017/CBO9781139381666
942			_2ddc _cEB
999			_c9056 _d9056