Missing data is common in clinical research. How these missing valuesare handled has a direct impact on the final study results. Existing medi-cal studies commonly use complete case analysis to...Show moreMissing data is common in clinical research. How these missing valuesare handled has a direct impact on the final study results. Existing medi-cal studies commonly use complete case analysis to remove observationswith missing values, which has the advantage that it is simple and easybut depletes the information in the original dataset, and may result in bi-ased estimated. Multiple imputation (MI) methods are often consideredmore reliable than complete case analysis, missing indicator methods andsingle imputation methods. However, recent research has shown that bycomparing a number of MI methods, particularly where the underlyingassumptions are undermined, some MI methods may cause more bias inmodel estimates than complete case analysis.To study which methods would perform better under which circum-stances, this thesis will perform a simulation study, comparing the resultsof the above-mentioned techniques under certain types of missingness,such as MCAR, MAR and MNAR. Complicated connections like miss-ingness is also correlated with survival time will be considered.The various parameter settings for the simulation study are based ona real case study where about 12% of the observations contain missingvalues for some variables. In addition to the basic MICE, two other multi-ple imputation methods are compared, one with interaction terms betweenthe full variables and the baseline hazard in the imputation model, and theother with a specific substantive model in the iteration. In this thesis, thesubstantive model is Cox model. The simulation studies show that theone with interaction terms is not significantly different from MICE andits improvements are of limited applicability. The one with the specificsubstantive model is more suitable for complex data types and when thereare strong correlations between covariates. Besides, basic MICE also per-forms well in data sets with a high proportion of missing binary covariates, while the missing indicator method produces large bias in many settings, even for full case studies.Show less