Methods for addressing missing data in psychiatric and developmental research

Objective: First, to provide information about best practices in handling missing data so that readers can judge the quality of research studies. Second, to provide more detailed information about missing data analysis techniques and software on the Journal's Web site at www.jaacap.com. Method: We focus our review of techniques on those that are based on the Missing at Random assumption and are either extremely popular because of their convenience or that are harder to employ but yield more precise inferences. Results: The literature regarding missing data indicates that deletion of observations with missing data can yield biased findings. Other popular methods for handling missing data, notably replacing missing values with means, can lead to confidence intervals that are too narrow as well as false identifications of significant differences (type I statistical errors). Methods such as multiple imputation and direct maximum likelihood estimation are often superior to deleting observations and other popular methods for handling missing data problems. Conclusions: Psychiatric and developmental researchers should consider using multiple imputation and direct maximum likelihood estimation rather than deleting observations with missing values.