Approaches to Handling Missing Data Within Developmental and Behavioral Pediatric Research

Brandon S. Aylward, MA,* Rawni A. Anderson, BA,* Timothy D. Nelson, PhD† Despite rigorous and conscientious attempts to obtain complete data, some percentage of missing or incomplete data in empirical research, including developmental and behavioral pediatrics, is generally the rule and not the exception.1 Missing data can result from a number of scenarios including participant nonresponse/ attrition (e.g., subject lost to follow-up or dropout and subject refusal) at a time point of measurement and/or item/scale nonresponse (e.g., subject may inadvertently skip an item or portion of a measure). Missing data may also be related to specific demographic or medical variables such as socioeconomic status or illness severity (e.g., those with lower socioeconomic status may be less likely to complete follow-up, less likelihood of completion of valid assessment in those with severity of medical condition). Researchers have used a wide variety of statistical techniques to manage missing data, and the method chosen can significantly affect the final results of a study. Ignoring missing data or using suboptimal data replacement strategies can have serious negative consequences.2 For example, missing data can result in reduced statistical power, decreased generalizability of findings, and biased parameter estimates.3,4 Furthermore, inconsistencies in reporting missing data pose challenges for evaluating research and replicating findings. Because the presence of missing data is inevitable, and the consequences of mismanagement can lead to misinterpretation of the study’s findings, careful consideration of, and familiarity with, best practices for handling missing data are important. The issue of missing data is particularly relevant to developmental and behavioral pediatric research. For example, missing data in longitudinal studies that involve infants born prematurely is typical (estimates of attrition between 30% and 50%). Within this population, factors that may increase the likelihood of subject dropout or being lost to follow-up include larger, less sick babies, infants from lower socioeconomic status households, babies born to single, young mothers, and those infants not born at a tertiary care hospital.5 The differential attrition of subjects can limit the generalizability of research findings. That is, results from an analysis of neurodevelopmental outcomes conducted for only those infants with complete data may not generalize to the entire population (e.g., those from lower socioeconomic status and babies born to single mothers) because of biases in subject loss. This is one of a vast array of examples in which missing data may be present in the field. Additional issues, such as patient test refusal, missed appointments for follow-up related to other factors, and administration errors, can all contribute to the magnitude of missing or incomplete data. Given the prevalence of missing data and the importance of selecting appropriate strategies for managing incomplete data, our focus is to review available options for handling missing data. Although the issue of missing data is not usually the main focus of inquiry within empirical research, the decision on the best way to address “missingness” should be of high importance.2 Fortunately, substantial advances have been made in the methodological approaches to missing data analyses over the past few decades.6 To assist developmental and behavioral pediatric researchers in making these decisions, we aim to (a) highlight the types and causes of missing data; (b) provide a conceptual overview of the various strategies for handling missing data; (c) discuss reporting practices with missing data, particularly within developmental and behavioral pediatric research; and (d) offer recommendations for best practices to consider in future research endeavors. This article is designed to provide a conceptual overview of these topics; more detailed discussions of specific missing data approaches are available elsewhere (e.g., see Refs. 2, 7, and 8).