title: [Variance estimation considering multistage sampling design in multistage complex sample analysis].
abstract: Multistage sampling is a frequently-used method in random sampling survey in public health. Clustering or independence between observations often exists in the sampling, often called complex sample, generated by multistage sampling. Sampling error may be underestimated and the probability of type I error may be increased if the multistage sample design was not taken into consideration in analysis. As variance (error) estimator in complex sample is often complicated, statistical software usually adopt ultimate cluster variance estimate (UCVE) to approximate the estimation, which simply assume that the sample comes from one-stage sampling. However, with increased sampling fraction of primary sampling unit, contribution from subsequent sampling stages is no more trivial, and the ultimate cluster variance estimate may, therefore, lead to invalid variance estimation. This paper summarize a method of variance estimation considering multistage sampling design. The performances are compared with UCVE and the method considering multistage sampling design by simulating random sampling under different sampling schemes using real world data. Simulation showed that as primary sampling unit (PSU) sampling fraction increased, UCVE tended to generate increasingly biased estimation, whereas accurate estimates were obtained by using the method considering multistage sampling design.
authors.affiliation: Division of Surveillance, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
authors.forename: Yichong
authors.initials: Y
authors.lastname: Li
authors.affiliation: Division of Surveillance, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
authors.forename: Yinjun
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Division of Surveillance, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
authors.forename: Limin
authors.initials: L
authors.lastname: Wang
authors.affiliation: Division of Surveillance, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
authors.forename: Mei
authors.initials: M
authors.lastname: Zhang
authors.affiliation: National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
authors.forename: Maigeng
authors.initials: M
authors.lastname: Zhou
mesh_terms: D000704:Analysis of Variance; D015982:Bias; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D012107:Research Design; D012984:Software