Approximate methods for analyzing semiparametric longitudinal models with nonignorable missing responses

Abstract

We often encounter missing data in longitudinal studies. When the missingness in longitudinal data is nonignorable, it is necessary to incorporate the missing data mechanism into the observed data likelihood function for a valid statistical inference. In this article, we propose and explore a novel semiparametric approach to estimating the regression parameters and variance components using a partially linear mixed model with nonignorable and nonmonotone missing responses. The finite sample properties of the proposed method are studied using Monte Carlo simulations, where our method is found to be very effective in capturing any curvilinear pattern in the mean response. The method is also illustrated using some actual longitudinal data obtained from a public health survey, referred to as the Health and Retirement Study (HRS). Journal of Statistical Research 2022, Vol. 56, No. 2, pp. 155-183