A two-step approach for analysis of nonignorable missing outcomes in longitudinal regression: an application to Upstate KIDS Study

摘要

BackgroundImperfect follow-up in longitudinal studies commonly leads to missing outcome data that can potentially bias the inference when the missingness is nonignorable; that is, the propensity of missingness depends on missing values in the data. In the Upstate KIDS Study, we seek to determine if the missingness of child development outcomes is nonigorable, and how a simple model assuming ignorable missingness would compare with more complicated models for a nonignorable mechanism.