title: [Identification and treatment of missing data].
abstract: Missing data plagues almost all surveys and researches. The occurrence of missing data will cause losses of original sample information and undermine the validity of the research results to some extents, so researchers should attach great importance to this problem. In this article, we introduced 3 kinds of missingness mechanism, namely missing completely at random, missing at random, and not missing at random. We summarized some common approaches to deal with missing data, including deletion, weighting approach, imputation and parameter likelihood method. Since these methods had its pros and cons, we should carefully select the proper way to handle missing data according to the missingness mechanism.
authors.affiliation: Department of Epidemiology and Health Statistics, Central South University, Changsha 410078,China.
authors.forename: Lin
authors.initials: L
authors.lastname: Shen
authors.forename: Qianhong
authors.initials: Q
authors.lastname: Chen
authors.forename: Hongzhuan
authors.initials: H
authors.lastname: Tan
mesh_terms: D003625:Data Collection; D003627:Data Interpretation, Statistical; D016013:Likelihood Functions