A local sensitivity analysis approach to longitudinal non-Gaussian data with non-ignorable dropout

Hui Xie
Statistics in Medicine 2008 July 20, 27 (16): 3155-77
Longitudinal non-Gaussian data subject to potentially non-ignorable dropout is a challenging problem. Frequently an analysis has to rely on some strong but unverifiable assumptions, among which ignorability is a key one. Sensitivity analysis has been advocated to assess the likely effect of alternative assumptions about dropout mechanism on such an analysis. Previously, Ma et al. applied a general index of local sensitivity to non-ignorability (ISNI) to measure the sensitivity of missing at random (MAR) estimates to small departures from ignorability for multivariate normal outcomes. In this paper, we extend the ISNI methodology to handle longitudinal non-Gaussian data subject to non-ignorable dropout. Specifically, we propose to quantify the sensitivity of inferences in the neighborhood of an MAR generalized linear mixed model for longitudinal data. Through a simulation study, we evaluate the performance of the proposed methodology. We then illustrate the methodology in one real example: smoking-cessation data.

Full Text Links

Find Full Text Links for this Article


You are not logged in. Sign Up or Log In to join the discussion.

Related Papers

Remove bar
Read by QxMD icon Read

Save your favorite articles in one place with a free QxMD account.


Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"