Multiple imputation (MI) is a commonly used approach to impute missing data. This thesis studies missing covariates in recurrent event data, and discusses ways to include the survival outcomes in the imputation model. Some MI methods under consideration are the event indicator D combined with, respectively, the right-censored event times T, the logarithm of T and the cumulative baseline hazard H0(T). After imputation, we can then proceed to the complete data analysis. The Cox proportional hazards (PH) model and the PWP model are chosen as the analysis models, and the coefficient estimates are of substantive interest. A Monte Carlo simulation study is conducted to compare different MI methods, the relative bias and mean square error will be used in the evaluation process. Furthermore, an empirical study based on cardiovascular disease event data which contains missing values will be conducted. Overall, the results show that MI based on the Nelson-Aalen estimate of H0(T) is preferred in most circumstances.

In this study, we examined survival for patients with micrometastases greater than 0.2mm but less than 2mm (pN1a) in comparison to node-negative (pN0) and macrometastatic node-positive (pN1b) patients. Data for patients diagnosed from 1988 to 1998 with TNM pathological T1-2 stage, pN0, and pN1a-b breast cancer with no distant metastasis was provided by Dr. P. Truong from BC Cancer Agency. Results obtained from the Kaplan-Meier estimators and the multivariable Cox Proportional Hazards Model analyses suggested that micrometastatic node-positive patients had worse survival than the node-negative patients, but better survival in comparison to the macrometastatic node-positive patients. Increasing number of positive nodes and larger values of the ratio of positive to excised nodes were significantly associated with worse survival.