.png){kind=icon}
.png){kind=icon}
Return
Sensitivity analysis for generalized estimating equation with non-ignorable missing data
.png){kind=icon}
OA
AI
DOI：10.1111/sjos.70060.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Many incomplete-data statistical inference procedures are developed under the missing at random (MAR) assumption. However, the MAR assumption has been criticized as being overly strong for real-data problems, and is unverifiable by using observed data. To handle data that are missing not at random (MNAR), sensitivity analysis has been proposed to investigate how conclusions are perturbed if the unverifiable MAR assumption is violated to a certain degree. This article proposes a new framework called multiple sensitivity models (MSMs) for performing general parameter estimation with the generalized estimating equation (GEE) method. Given user-specified sensitivity parameters, a range of estimators is derived by solving the roots of the bounds of MSM-assisted GEEs. Furthermore, we derive a representation for the proposed estimator so that it can be decomposed into several simpler estimators. It allows us to investigate the impact of different missing patterns. An asymptotically valid percentile bootstrap confidence region (CR) is also proposed. Theoretical justification is provided together with empirical evidence, which verifies the usefulness of the proposal's sensitivity analysis.
Keywords:
decomposition theorem
incomplete-data inference
missing not at random
partially identifiable models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
S
IF:
1
Papers:
52
Citations:
0