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A Two‐Step Approach for Analysis of Nonignorable Missing Outcomes in Longitudinal Regression: an Application to Upstate KIDS Study
Author(s) -
Liu Danping,
Yeung Edwina H.,
McLain Alexander C.,
Xie Yunlong,
Buck Louis Germaine M.,
Sundaram Rajeshwari
Publication year - 2017
Publication title -
paediatric and perinatal epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.667
H-Index - 88
eISSN - 1365-3016
pISSN - 0269-5022
DOI - 10.1111/ppe.12382
Subject(s) - missing data , outcome (game theory) , inference , econometrics , propensity score matching , statistics , random effects model , computer science , medicine , mathematics , artificial intelligence , meta analysis , mathematical economics
Abstract Background Imperfect 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 nonignorable, and how a simple model assuming ignorable missingness would compare with more complicated models for a nonignorable mechanism. Methods To correct for nonignorable missingness, the shared random effects model ( SREM ) jointly models the outcome and the missing mechanism. However, the computational complexity and lack of software packages has limited its practical applications. This paper proposes a novel two‐step approach to handle nonignorable missing outcomes in generalized linear mixed models. We first analyse the missing mechanism with a generalized linear mixed model and predict values of the random effects; then, the outcome model is fitted adjusting for the predicted random effects to account for heterogeneity in the missingness propensity. Results Extensive simulation studies suggest that the proposed method is a reliable approximation to SREM , with a much faster computation. The nonignorability of missing data in the Upstate KIDS Study is estimated to be mild to moderate, and the analyses using the two‐step approach or SREM are similar to the model assuming ignorable missingness. Conclusions The two‐step approach is a computationally straightforward method that can be conducted as sensitivity analyses in longitudinal studies to examine violations to the ignorable missingness assumption and the implications relative to health outcomes.