A comparison of analytic methods for non-random missingness of outcome data

Abstract

Missing outcome values occur frequently in survey data and are rarely missing randomly. Depending on the pattern of missingness, the choice of analytic method has implications for accuracy of the estimated outcome distribution as well as multivariate models. Data from a study of patterns of care in disabled elders were used to evaluate several common methods when missingness of the outcome was non-random. Results from single and multiple model-based imputation were compared with results from complete-case analysis and mean imputation. By ignoring nonrespondents' covariate information, the latter two methods yielded biased estimates of population means. Mean imputation and single model-based imputation underestimated standard errors by treating imputed values as if they were observed. Mean imputation also distorted the relationship between the outcome and predictors. Multiple model-based imputation provided an easily implemented method of adjustment for non-random non-response in both univariate and multivariate analyses.