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Items: 1 to 20 of 73

1.

Missing data: our view of the state of the art.

Schafer JL, Graham JW.

Psychol Methods. 2002 Jun;7(2):147-77.

PMID:
12090408
2.

Can one assess whether missing data are missing at random in medical studies?

Potthoff RF, Tudor GE, Pieper KS, Hasselblad V.

Stat Methods Med Res. 2006 Jun;15(3):213-34.

PMID:
16768297
3.

Dealing with missing data in a multi-question depression scale: a comparison of imputation methods.

Shrive FM, Stuart H, Quan H, Ghali WA.

BMC Med Res Methodol. 2006 Dec 13;6:57.

4.
5.

Methods for handling missing data in palliative care research.

Fielding S, Fayers PM, Loge JH, Jordhøy MS, Kaasa S.

Palliat Med. 2006 Dec;20(8):791-8.

PMID:
17148533
6.

[Multiple imputation of missing at random data: General points and presentation of a Monte-Carlo method].

Cottrell G, Cot M, Mary JY.

Rev Epidemiol Sante Publique. 2009 Oct;57(5):361-72. doi: 10.1016/j.respe.2009.04.011. Epub 2009 Aug 11. French.

PMID:
19674855
7.

Out of sight, not out of mind: strategies for handling missing data.

Buhi ER, Goodson P, Neilands TB.

Am J Health Behav. 2008 Jan-Feb;32(1):83-92.

PMID:
18021036
8.

Handling missing data in self-report measures.

Fox-Wasylyshyn SM, El-Masri MM.

Res Nurs Health. 2005 Dec;28(6):488-95. Review.

PMID:
16287052
9.

Advanced statistics: missing data in clinical research--part 2: multiple imputation.

Newgard CD, Haukoos JS.

Acad Emerg Med. 2007 Jul;14(7):669-78.

10.

Using the outcome for imputation of missing predictor values was preferred.

Moons KG, Donders RA, Stijnen T, Harrell FE Jr.

J Clin Epidemiol. 2006 Oct;59(10):1092-101. Epub 2006 Jun 19.

PMID:
16980150
11.

Comparison of data analysis strategies for intent-to-treat analysis in pre-test-post-test designs with substantial dropout rates.

Salim A, Mackinnon A, Christensen H, Griffiths K.

Psychiatry Res. 2008 Sep 30;160(3):335-45. doi: 10.1016/j.psychres.2007.08.005. Epub 2008 Aug 20.

PMID:
18718673
12.

DNA microarray data imputation and significance analysis of differential expression.

Jörnsten R, Wang HY, Welsh WJ, Ouyang M.

Bioinformatics. 2005 Nov 15;21(22):4155-61. Epub 2005 Aug 23.

13.

Imputation strategies for missing continuous outcomes in cluster randomized trials.

Taljaard M, Donner A, Klar N.

Biom J. 2008 Jun;50(3):329-45. doi: 10.1002/bimj.200710423. Review.

PMID:
18537126
14.

The impact of missing data and how it is handled on the rate of false-positive results in drug development.

Barnes SA, Mallinckrodt CH, Lindborg SR, Carter MK.

Pharm Stat. 2008 Jul-Sep;7(3):215-25.

PMID:
17853425
15.
16.

A comparison of imputation techniques for handling missing data.

Musil CM, Warner CB, Yobas PK, Jones SL.

West J Nurs Res. 2002 Nov;24(7):815-29.

PMID:
12428897
17.

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

Sehgal MS, Gondal I, Dooley LS.

Bioinformatics. 2005 May 15;21(10):2417-23. Epub 2005 Feb 24.

18.

Handling missing quality of life data in HIV clinical trials: what is practical?

Fairclough DL, Thijs H, Huang IC, Finnern HW, Wu AW.

Qual Life Res. 2008 Feb;17(1):61-73. Epub 2007 Dec 11.

PMID:
18071926
19.

Evaluation of software for multiple imputation of semi-continuous data.

Yu LM, Burton A, Rivero-Arias O.

Stat Methods Med Res. 2007 Jun;16(3):243-58.

PMID:
17621470
20.

A multiple imputation model for imputing missing physiologic data in the national trauma data bank.

Moore L, Hanley JA, Turgeon AF, Lavoie A, Emond M.

J Am Coll Surg. 2009 Nov;209(5):572-9. doi: 10.1016/j.jamcollsurg.2009.07.004. Epub 2009 Sep 17.

PMID:
19854396
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