  Missing data is a common problem in clinical data collection, which causes
difficulty in the statistical analysis of such data. In this article, we
consider the problem under a framework of a semiparametric partially linear
model when observations are subject to missingness with complex patterns. If
the correct model structure of the additive partially linear model is
available, we propose to use a new imputation method called Partial Replacement
IMputation Estimation (PRIME), which can overcome problems caused by incomplete
data in the partially linear model. Also, we use PRIME in conjunction with
model averaging (PRIME-MA) to tackle the problem of unknown model structure in
the partially linear model. In simulation studies, we use various error
distributions, sample sizes, missing data rates, covariate correlations, and
noise levels, and PRIME outperforms other methods in almost all cases. With an
unknown correct model structure, PRIME-MA has satisfactory performance in terms
of prediction, while slightly worse than PRIME. Moreover, we conduct a study of
influential factors in Pima Indians Diabetes data, which shows that our method
performs better than the other models.
