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Parametric and Semiparametric Model‐Based Estimates of the Finite Population Mean for Two‐Stage Cluster Samples with Item Nonresponse
Author(s) -
Yuan Ying,
Little Roderick J. A.
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00816.x
Subject(s) - covariate , missing data , weighting , imputation (statistics) , econometrics , statistics , propensity score matching , population , computer science , parametric statistics , mathematics , demography , medicine , radiology , sociology
Summary This article concerns item nonresponse adjustment for two‐stage cluster samples. Specifically, we focus on two types of nonignorable nonresponse: nonresponse depending on covariates and underlying cluster characteristics, and depending on covariates and the missing outcome. In these circumstances, standard weighting and imputation adjustments are liable to be biased. To obtain consistent estimates, we extend the standard random‐effects model by modeling these two types of missing data mechanism. We also propose semiparametric approaches based on fitting a spline on the propensity score, to weaken assumptions about the relationship between the outcome and covariates. These new methods are compared with existing approaches by simulation. The National Health and Nutrition Examination Survey data are used to illustrate these approaches.