OBJECTIVES: Researchers use the Healthcare Cost and Utilization Project (HCUP) Cost-to-Charge Ratio (CCR) File to translate total charges in the Nationwide Inpatient Sample (NIS) into accurate costs. We investigated the impact on cost estimates using four techniques for handling missing CCRs. METHODS: This is a cross-sectional study using nationally-representative hospitalization data in the US in 2011. We investigated four techniques including: 1) complete case analysis; 2a) using a reweighting technique recommended by HCUP; 2b) reweighting by adjustment cells; and 3) using hotdeck imputation by adjustment cells. We used five disease cohorts chosen to represent different sample sizes. We created a disease cohort data set with non-missing CCRs as a benchmark complete data set. We picked one state at a time and set their CCRs to missing to create a data set with missing CCR data. We compared the CCR-adjusted cost estimates (mean and total) from both data sets. To evaluate the quality of an estimator, we assessed bias, variance, and the mean squared error (MSE) of an estimator. RESULTS: The range of missing rates was 1%-12%. The majority of states (>84%) had missing CCR data in the range of 1% to 4%. In general, biases in magnitude and MSE of mean adjusted costs increased as missing rates increased. Complete case analysis, the reweighting technique recommended by HCUP, and reweighting by adjustment cells performed similarly in mean cost estimation. Reweighting techniques performed better in total cost estimation. Complete case analysis underestimated total costs and became worse as missing rates increased. CONCLUSIONS: In NIS data, to handle missing CCRs, when missing data <4%, researchers will obtain similar mean cost estimates using complete case analysis, the reweighting technique recommended by HCUP, or reweighting by adjustment cells. However, if total cost is the estimate of interest, researchers should adopt either reweighting techniques in order to avoid underestimation.