@article {9632,
	title = {Semi-parametric methods of handling missing data in mortal cohorts under non-ignorable missingness.},
	journal = {Biometrics},
	volume = {74},
	year = {2018},
	pages = {1427-1437},
	abstract = {We propose semi-parametric methods to model cohort data where repeated outcomes may be missing due to death and non-ignorable dropout. Our focus is to obtain inference about the cohort composed of those who are still alive at any time point (partly conditional inference). We propose: i) an inverse probability weighted method that upweights observed subjects to represent subjects who are still alive but are not observed; ii) an outcome regression method that replaces missing outcomes of subjects who are alive with their conditional mean outcomes given past observed data; and iii) an augmented inverse probability method that combines the previous two methods and is double robust against model misspecification. These methods are described for both monotone and non-monotone missing data patterns, and are applied to a cohort of elderly adults from the Health and Retirement Study. Sensitivity analysis to departures from the assumption that missingness at some visit t is independent of the outcome at visit t given past observed data and time of death is used in the data application.},
	keywords = {Data collection, Datasets, Survey Methodology},
	issn = {1541-0420},
	doi = {10.1111/biom.12891},
	author = {Wen, Lan and Shaun R Seaman}
}
