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- W2608045970 abstract "Longitudinal studies are very common in public health and medical sciences. Missing values are not uncommon with longitudinal studies. Ignoring the missing values in the analysis of longitudinal data leads to biased estimates. Valid inference about longitudinal data must incorporate the missing data model into the analysis. Several approaches have been proposed to obtain valid inference in the presence of missing values. One of these approaches is the imputation techniques. Imputation techniques range from single imputation (the missing value is imputed by a single observation) to multiple imputation, where the missing value is imputed by a fixed number of observations. In this article we propose a new imputation strategy to handle missing values in longitudinal data. The new strategy depends on imputing the missing values with donors from the observed values. The donors represent quantiles of the observed data. This imputation strategy is applicable if the missing data mechanism is missing not at random. The proposed technique is applied to a real data set of antidepressant clinical trial. Also, a simulation study is conducted to evaluate the proposed strategy." @default.
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- W2608045970 date "2013-01-01" @default.
- W2608045970 modified "2023-09-27" @default.
- W2608045970 title "A New Imputation Strategy for Incomplete Longitudinal Data" @default.
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