LEADER 02305nam  2200493   450 
001 9910814694303321
005 20200520144314.0
010   $a1-118-57363-3
035   $a(CKB)24989749000041
035   $a(NjHacI)9924989749000041
035   $a(Au-PeEL)EBL1691883
035   $a(CaPaEBR)ebr10874726
035   $a(CaONFJC)MIL611516
035   $a(OCoLC)864808991
035   $a(MiAaPQ)EBC1691883
035   $a(EXLCZ)9924989749000041
100   $a20140605h20142014  uy             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aApplied missing data analysis in the health sciences /$fXiao-Hua Zhou [and three others]
210  1$aHoboken, New Jersey :$cJohn Wiley & Sons,$d2014.
210  4$d2014
215   $a1 online resource (256 pages)
225 1 $aWiley Series in Statistics in Practice
320   $aIncludes bibliographical references and index.
327   $aMissing 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.
330   $aA modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine.
410  0$aStatistics in practice.
606   $aMedical sciences$xStudy and teaching
606   $aMedicine$xResearch
615  0$aMedical sciences$xStudy and teaching.
615  0$aMedicine$xResearch.
676   $a610.711
702   $aZhou$b Xiao-Hua
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910814694303321
996   $aApplied missing data analysis in the health sciences$93913673
997   $aUNINA