Handling Missing Items in Quality of Life Studies

Abstract

Abstract Three methods of handling missing items in Quality of Life (QOL) studies are compared using data from a clinical trial. Complete case analysis can lead to a selection bias and a loss of precision if there is a low answer rate to one item. The two other methods, simple mean imputation and multiple imputation, lead to similar results. We conclude that when the scale has been carefully validated in the population of interest and includes only items with a high response rate (e.g., a mean response rate greater than 95% for the items of a given subscale), a simple imputation method can be used to handle incomplete observations.