Dealing with missing data in observational health care outcome analyses

Abstract

Observational outcome analyses appear frequently in the health research literature. For such analyses, clinical registries are preferred to administrative databases. Missing data are a common problem in any clinical registry, and pose a threat to the validity of observational outcomes analyses. Faced with missing data in a new clinical registry, we compared three possible responses: exclude cases with missing data; assume that the missing data indicated absence of risk; or merge the clinical database with an existing administrative database. The predictive model derived using the merged data showed a higher C statistic ( C = 0.770), better model goodness-of-fit as measured in a decile-of-risk analysis, the largest gradient of risk across deciles (46.3), and the largest decrease in deviance (−2 log likelihood = 406.2). The superior performance of the enhanced data model supports the use of this “enhancement” methodology and bears consideration when researchers are faced with nonrandom missing data.