Abstract
Epidemiological cohort study that adopts a two-phase design raises serious issue on how to treat a fairly large amount of missing val ues 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 aforemen tioned 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 pro vides a practical and general framework for the estimation of epidemiological characteristics in two-phase sampling studies.
Highlights
In longitudinal epidemiological studies where subjects enrolled are followed at a series of time points for the examination of characteristics related to the disease or condition of interest, missing values always occur for various reasons
It was shown that likelihood based approach ignoring the missing-data process provides valid inference for Missing At Random (MAR) data if the variables associated with the missing-data process is included in the model; and it can be potentially biased for Missing Not At Random (MNAR) data (Little and Rubin, 1987)
The incidence rate of subjects who were lost to follow-up is different from the incidence rates of the respondents in the estimation set
Summary
In longitudinal epidemiological studies where subjects enrolled are followed at a series of time points (or data collection waves) for the examination of characteristics related to the disease or condition of interest, missing values always occur for various reasons. This phenomenon is more pronounced in dementia related cohort studies targeting on the elderly because the study subjects are more susceptible to illness or death. It was shown that likelihood based approach ignoring the missing-data process provides valid inference for MAR data if the variables associated with the missing-data process is included in the model; and it can be potentially biased for MNAR data (Little and Rubin, 1987). A sensitivity analysis is usually required to examine the impact of various assumptions on the result of the analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
