000152307 001__ 152307
000152307 005__ 20180122225628.0
000152307 0247_ $$2Other$$ast0190
000152307 037__ $$a199-2016-2682
000152307 037__ $$a199-2016-3290
000152307 041__ $$aen_US
000152307 245__ $$aAnalyzing longitudinal data in the presence of informative drop-out: The jmre1 command
000152307 260__ $$c2010
000152307 269__ $$a2010
000152307 270__ $$mnpantaz@med.uoa.gr$$pPantazis,   Nikos
000152307 270__ $$mgtouloum@med.uoa.gr$$pTouloumi,   Giota
000152307 300__ $$a26
000152307 336__ $$aJournal Article
000152307 520__ $$aMany studies in various research areas have designs that involve repeated measurements over time of a continuous variable across a group of subjects. A frequent and serious problem in such studies is the occurrence of missing data. In many cases, missing data are caused by an event that leads to a premature termination of the series of repeated measurements on some subjects. When the probability of the occurrence of this event is related to the subject-specific underlying trend of the variable of interest, this missingness process is called informative censoring or informative drop-out. Standard likelihood-based methods (for example, linear mixed models) fail to give consistent estimates. In such cases, one needs to apply methods that simultaneously model the observed data and the missingness process. In this article, we review a method proposed by Touloumi et al. (1999, Statistics in Medicine 18: 1215–1233) to adjust for informative drop-out in longitudinal data analysis. We also present the jmre1 command, which can be used to fit the proposed model. The estimation method combines the restricted iterative generalized least-squares method with a nested expectation-maximization algorithm. The method is implemented mainly using Stata’s matrix programming language, Mata. Our example is derived from the epidemiology of the HIV infection.
000152307 542__ $$fLicense granted by Lisa Gilmore (lgilmore@stata.com) on 2013-07-10T14:39:49Z (GMT):

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000152307 650__ $$aResearch Methods/ Statistical Methods
000152307 6531_ $$ajmre1
000152307 6531_ $$ajmre1_p
000152307 6531_ $$adatajoint1
000152307 6531_ $$amissing data
000152307 6531_ $$ainformative censoring
000152307 6531_ $$ainformative drop-out
000152307 6531_ $$alongitudinal data
000152307 700__ $$aPantazis, Nikos
000152307 700__ $$aTouloumi, Giota
000152307 773__ $$d2nd Quarter$$jVolume 10$$kNumber 2$$o251$$q226$$tStata Journal
000152307 8564_ $$s280192$$uhttp://ageconsearch.umn.edu/record/152307/files/sjart_st0190.pdf
000152307 887__ $$ahttp://purl.umn.edu/152307
000152307 909CO $$ooai:ageconsearch.umn.edu:152307$$pGLOBAL_SET
000152307 912__ $$nSubmitted by Lisa Gilmore (lgilmore@stata.com) on 2013-07-10T14:41:49Z
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  Previous issue date: 2010
000152307 982__ $$gStata Journal>Volume 10, Number 2, 2nd Quarter 2010
000152307 980__ $$a199