Norris CM, Ghali WA, Knudtson ML, Saunders LD, Naylor CD. Dealing with missing data in observational health care outcome analysis. J Clin Epidemiol. 2000 Apr;53:377-383.

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.

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