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Application of Multiple Imputation to Data from Two-phase Sampling: Estimation of the Incidence Rate of Cognitive Impairment
並列摘要
Epidemiological cohort study that adopts a two-phase design raises serious issue on how to treat a fairly large amount of missing values that are either Missing At Random (MAR) due to the study design or potentially Missing Not At Random (MNAR) due to non-response and loss to follow-up. Cognitive impairment (CI) is an evolving concept that needs epidemiological characterization for its maturity. In this work, we attempt to estimate the incidence rate CI by accounting for the aforementioned missing-data process. We consider baseline and first follow-up data of 2191 African-Americans enrolled in a prospective epidemiological study of dementia that adopted a two-phase sampling design. We developed a multiple imputation procedure in the mixture model framework that can be easily implemented in SAS. Sensitivity analysis is carried out to assess the dependence of the estimates on specific model assumptions. It is shown that African-Americans in the age of 65-75 have much higher incidence rate of CI than younger or older elderly. In conclusion, multiple imputation provides a practical and general framework for the estimation of epidemiological characteristics in two-phase sampling studies.
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延伸閱讀
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