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Abstract Background: After a prognostic model has been developed it is important to evaluate its performance in an independent dataset, often referred as external validation. However, data sets used to evaluate prognostic models are frequently too small, and the handling of missing data has been shown to be poor. Method: Using resampling methods with large real dataset (THIN), we investigate the impact of the missing data in the validation cohort on the evaluation of performance of the QRISK2 model for predicting the 10-year risk of developing cardiovascular disease. We also include an examination of the influence of varying the sample size. Five levels of missingness (varying from 5% to 75%) were imposed using a missing at random (MAR) mechanism, as well as varying the sample size (number of events; from 10 to 1000). Four missing data methods were applied: complete case analysis, multiple imputation using regression switching, multiple imputation using predictive mean matching and multiple imputation using flexible additive imputation models. The performance of QRISK2 was assessed by calculating measures of discrimination (c-index, D-statistic) and calibration (calibration plot). The impact of the four different approaches for handling the missing data was examined by calculating the percentage bias. Results: When the amount of missing data was small, there was little difference between the various approaches for handling missing data. However, as the amount of missing data increased, multiple imputation methods provided least biased estimates and better performance than the complete case analysis. These findings were also consistent over all the sample size scenarios examined. Conclusion: Our study provides insight into the impact and handling of missing data on model performance. In all scenarios, regardless of the sample size, multiple imputation outperformed complete-case analyses and should be considered when validating a prognostic model

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