Sensitivity analysis for generalized estimating equation with non‐ignorable missing data
针对非随机缺失数据，提出多重敏感性模型框架，通过广义估计方程推导估计量范围，并给出渐近有效的置信区间，帮助研究者评估缺失假设违背时结论的稳健性。
Abstract 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.