Repository logo
 

Semi-parametric methods of handling missing data in mortal cohorts under non-ignorable missingness.


Change log

Authors

Seaman, Shaun R 

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.

Description

Keywords

Dropout, Generalized estimating equation, Intermittent missing, Longitudinal data, Non-ignorable, Partly conditional inference, Sensitivity analysis, Aged, Aged, 80 and over, Bias, Biometry, Cohort Studies, Computer Simulation, Death, Humans, Longitudinal Studies, Probability, Regression Analysis

Journal Title

Biometrics

Conference Name

Journal ISSN

0006-341X
1541-0420

Volume Title

74

Publisher

Oxford University Press (OUP)
Sponsorship
MRC (unknown)
MRC (unknown)