@article{Helms2011,
author = "R. W. Helms and L. H. Reece",
abstract = "Missing not at random (MNAR) post-dropout missing data from a longitudinal
	clinical trial result in the collection of obiased data,o which leads
	to biased estimators and tests of corrupted hypotheses. In a full
	rank linear model analysis the model equation, E[Y]=X, leads to the
	definition of the primary parameter =(X'X)-1X'E[Y], and the definition
	of linear secondary parameters of the form =L=L(X'X)-1X'E[Y], including,
	for example, a parameter representing a otreatment effect.o These
	parameters depend explicitly on E[Y], which raises the questions:
	What is E[Y] when some elements of the incomplete random vector Y
	are not observed and MNAR, or when such a Y is ocompletedo via imputation?
	We develop a rigorous, readily interpretable definition of E[Y] in
	this context that leads directly to definitions of , [image omitted],
	[image omitted], and the extent of hypothesis corruption. These definitions
	provide a basis for evaluating, comparing, and removing biases induced
	by various linear imputation methods for MNAR incomplete data from
	longitudinal clinical trials. Linear imputation methods use earlier
	data from a subject to impute values for post-dropout missing values
	and include oLast Observation Carried Forwardo (LOCF) and oBaseline
	Observation Carried Forwardo (BOCF), among others. We illustrate
	the methods of evaluating, comparing, and removing biases and the
	effects of testing corresponding corrupted hypotheses via a hypothetical
	but very realistic longitudinal analgesic clinical trial. ",
doi = "10.1080/10543406.2011.550097",
journal = "Journal of Biopharmaceutical Statistics",
number = "2",
pages = "226--251",
title = "{D}efining, {E}valuating, and {R}emoving {B}ias {I}nduced by {L}inear {I}mputation
	in {L}ongitudinal {C}linical {T}rials with {MNAR} {M}issing {D}ata",
volume = "21",
year = "2011",
}