Imputation technique: replacing missing values in longitudinal data

Abstract

In longitudinal studies, many cases are found missing in the follow-up data. These missing cases may arise due to item non response or unit non response. A data set with missing observations is often completed by using imputed values. The unit non response is usually carried out by some weighting adjustment. The item non response is made by the method called imputation. Imputation is a technique to replace a missing/incomplete or strange value with a more or less artificial value. There are plenty of methods available to impute the missing values in a longitudinal data. Imputation is useful because they make the data set easier to analyze, ensure consistency between the results from different analyses and reduce non response bias from item non response.  But it is not necessary that the imputed value reduces the bias of the data, sometimes they may lead more bias also. It depends on the imputation procedure which we choose and also the form of estimate. The aim of this article is to sensitize doctors and post-graduate medical students about this useful analytical technique.