Overview of missing data techniques.
Missing data frequently arise in the course of research studies. Understanding the mechanism that led to the missing data is important in order for investigators to be able to perform analyses that will lead to proper inference. This chapter will review different missing data mechanisms, including random and non-random mechanisms. Basic methods will be presented using examples to illustrate approaches to analyzing data in the presence of missing data.
D'Agostino
Ralph B
RB
Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
D000818:Animals; D049109:Cell Proliferation / Q000187:drug effects; D003627:Data Interpretation, Statistical; D004798:Enzymes / Q000378:metabolism; D008297:Male; D051379:Mice; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000628:therapy; D011506:Proteins / Q000378:metabolism
D004798:Enzymes; D011506:Proteins