Imputation of missing longitudinal data: a comparison of methods.
OBJECTIVE
Missing information is inevitable in longitudinal studies, and can result in biased estimates and a loss of power. One approach to this problem is to impute the missing data to yield a more complete data set. Our goal was to compare the performance of 14 methods of imputing missing data on depression, weight, cognitive functioning, and self-rated health in a longitudinal cohort of older adults.


METHODS
We identified situations where a person had a known value following one or more missing values, and treated the known value as a "missing value." This "missing value" was imputed using each method and compared to the observed value. Methods were compared on the root mean square error, mean absolute deviation, bias, and relative variance of the estimates.


RESULTS
Most imputation methods were biased toward estimating the "missing value" as too healthy, and most estimates had a variance that was too low. Imputed values based on a person's values before and after the "missing value" were superior to other methods, followed by imputations based on a person's values before the "missing value." Imputations that used no information specific to the person, such as using the sample mean, had the worst performance.


CONCLUSIONS
We conclude that, in longitudinal studies where the overall trend is for worse health over time and where missing data can be assumed to be primarily related to worse health, missing data in a longitudinal sequence should be imputed from the available longitudinal data for that person.
Departments of Biostatistics and Health Services, University of Washington, 1959 Northeast Pacific Avenue, Box 357232, Seattle, WA 98195, USA. mundahl@u.washington.edu
Jean Mundahl
JM
Engels
Paula
P
Diehr
D000368:Aged; D000704:Analysis of Variance; D015982:Bias; D003327:Coronary Disease / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D003863:Depression / Q000453:epidemiology; D005260:Female; D006304:Health Status; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D012107:Research Design; D012307:Risk Factors; D020521:Stroke / Q000453:epidemiology; D014481:United States / Q000453:epidemiology