LAUSR

OBJECTIVE Although several statistical methods for adjusting for missing data have been developed and are widely applied in research, few studies have investigated these methods in adjusting for missingness in datasets that aim to estimate the prevalence of dementia. We attempted to develop a more feasible approach for handling missingness in a cross-sectional study among elderly. METHODS Five methods of estimating prevalence, including stratified weighting (SW), inverse-probability weighting (IPW), hot deck imputation (HDI), ordinal logistic regression (OLR) and multiple imputation (MI), were applied to handle the missing data yielded by a dataset that include 2231 non-responders. RESULTS Compared with the results of the complete case analysis, the differences in the prevalence rates of dementia and mild cognitive impairment (MCI) calculated by the prevalence-estimating methods after adjusting for non-responders were less than 7% and 6%, respectively. In contrast to the results of other methods, the estimated prevalence of dementia and MCI calculated by MI increased when more predictive factors were included, and the lowest rate of missing data was achieved using MI. Using the participants' ages, the cognitive screening sores and activity of daily life sores as predictive variables when correcting for missingness induced relatively larger effects on the estimated dementia prevalence. CONCLUSIONS When adjusting for missingness while estimating the prevalence of dementia in cross-sectional studies, a simple method, such as SW, is recommended when limited information is available, whereas MI is the preferred method when additional information is available. Further simulation studies are needed to determine the optimal approach.