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10.2215/CJN.10141013 http://scihub22266oqcxt.onion/10.2215/CJN.10141013 C4078963!4078963!24509298     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+J+Am+Soc+Nephrol 2014 ; 9 (7): 1328-35 Nephropedia Template TP {{tp|p=24509298|t=2014. Addressing Missing Data in Clinical Studies of Kidney Diseases |pdf=|usr=}}{{24509298}} gab.com Text Addressing Missing Data in Clinical Studies of Kidney Diseases JO: Clin J Am Soc Nephrol http://www.kidney.de/pget.php?&tw=1&pmid=24509298 #FOAvip Missing data constitute a problem present in all studies of medical research. The most common approach to handling missing data?complete case analysis?relies on assumptions about missing data that rarely hold in practice. The implications of this approach are biased and inefficient descriptions of relationships of interest. Here, various approaches for handling missing data in clinical studies are described. In particular, this work promotes the use of multiple imputation methods that rely on assumptions about missingness that are more flexible than those assumptions relied on by the most common method in use. Furthermore, multiple imputation methods are becoming increasingly more accessible in mainstream statistical software packages, making them both a sound and practical choice. The use of multiple imputation methods is illustrated with examples pertinent to kidney research, and concrete guidance on their use is provided. Twit Text FOAVip Addressing Missing Data in Clinical Studies of Kidney Diseases ????Clin J Am Soc Nephrol http://www.kidney.de/pget.php?&tw=1&pmid=24509298 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24509298 #FOAvip English Wikipedia <ref>{{Cite pmid|24509298}}</ref> Addressing Missing Data in Clinical Studies of Kidney Diseases #MMPMID24509298 Montez-Rath ME; Winkelmayer WC; Desai M Clin J Am Soc Nephrol 2014[Jul]; 9 (7): 1328-35 PMID24509298show ga Missing data constitute a problem present in all studies of medical research. The most common approach to handling missing data?complete case analysis?relies on assumptions about missing data that rarely hold in practice. The implications of this approach are biased and inefficient descriptions of relationships of interest. Here, various approaches for handling missing data in clinical studies are described. In particular, this work promotes the use of multiple imputation methods that rely on assumptions about missingness that are more flexible than those assumptions relied on by the most common method in use. Furthermore, multiple imputation methods are becoming increasingly more accessible in mainstream statistical software packages, making them both a sound and practical choice. The use of multiple imputation methods is illustrated with examples pertinent to kidney research, and concrete guidance on their use is provided. äAddressing Missing Data in Clinical Studies of Kidney Diseases (epmc).pdf DeepDyve Pubget Overpricing