Impact of statistical approaches for handling missing data on XXXX center quality.

@entity1 @entity772 @entity130
OBJECTIVE: To determine whether imputed data can be used to produce unbiased hospital quality measures. BACKGROUND: Different methods for handling missing data may influence which hospitals are designated as quality outliers. METHODS: Monte-Carlo simulation study based on 63,020 @entity1 with no missing data in 68 hospitals using the National @entity130 Data Bank (NTDB, version 6.1). @entity1 were assigned missing data for the motor component of the Glasgow @entity772 scale (GCS) conditional on their observed clinical risk factors. Multiple imputation was then used to "fill in" the missing data. Hospital risk-adjusted quality measures (observed-to-expected mortality ratio) based either on (1) imputed data, (2) excluding @entity1 with missing data (complete case analysis), or (3) excluding the predictor with missing data were compared with hospital quality based on the true data (no missing data). Pair-wise comparisons of hospital quality were performed using the intraclass correlation coefficient (ICC) and the kappa statistic. RESULTS: With 10% of the data missing, the level of agreement between multiple imputation and the true data (ICC = 0.99 and kappa = 0.87) was better compared with the level of agreement between complete case analysis and the true data (ICC = 0.93 and kappa = 0.62). Excluding the predictor (motor GCS) with missing data from the risk adjustment model resulted in the least amount of agreement with quality assessment based on the true data (ICC = 0.88 and kappa = 0.46). CONCLUSION: Multiple imputation can be used to impute missing data and yields hospital quality measures that are nearly identical to those based on the true data. Simply excluding @entity1 with missing data or excluding risk factors with missing data from hospital quality assessment yields substantially inferior quality measures.
