@article{Liu2011a,
author = "G. F. Liu and X. J. Zhan",
abstract = "It is important yet challenging to choose an appropriate analysis
	method for the analysis of repeated binary responses with missing
	data. The conventional method using the last observation carried
	forward (LOCF) approach can be biased in both parameter estimates
	and hypothesis tests. The generalized estimating equations (GEE)
	method is valid only when missing data are missing completely at
	random, which may not be satisfied in many clinical trials. Several
	random-effects models based on likelihood or pseudo-likelihood methods
	and multiple-imputation-based methods have been proposed in the literature.
	In this paper, we evaluate the random-effects models with full- or
	pseudo-likelihood methods, GEE, and several multiple-imputation approaches.
	Simulations are used to compare the results and performance among
	these methods under different simulation settings. ",
doi = "10.1080/10543401003687129",
journal = "Journal of Biopharmaceutical Statistics",
number = "3",
pages = "371--392",
title = "{C}omparisons of {M}ethods for {A}nalysis of {R}epeated {B}inary {R}esponses
	with {M}issing {D}ata",
volume = "21",
year = "2011",
}