Examining solutions to missing data in longitudinal nursing research

Purpose: Longitudinal studies are highly valuable in pediatrics because they provide useful data about developmental patterns of child health and behavior over time. When data are missing, the value of the research is impacted. The study\u27s purpose was to (1) introduce a three-step approach to assess and address missing data and (2) illustrate this approach using categorical and continuous-level variables from a longitudinal study of premature infants. Methods: A three-step approach with simulations was followed to assess the amount and pattern of missing data and to determine the most appropriate imputation method for the missing data. Patterns of missingness were Missing Completely at Random, Missing at Random, and Not Missing at Random. Missing continuous-level data were imputed using mean replacement, stochastic regression, multiple imputation, and fully conditional specification (FCS). Missing categorical-level data were imputed using last value carried forward, hot-decking, stochastic regression, and FCS. Simulations were used to evaluate these imputation methods under different patterns of missingness at different levels of missing data. Results: The rate of missingness was 16–23% for continuous variables and 1–28% for categorical variables. FCS imputation provided the least difference in mean and standard deviation estimates for continuous measures. FCS imputation was acceptable for categorical measures. Results obtained through simulation reinforced and confirmed these findings. Practice Implications: Significant investments are made in the collection of longitudinal data. The prudent handling of missing data can protect these investments and potentially improve the scientific information contained in pediatric longitudinal studies