This dissertation is aiming to find out the impact of the Gramm-Leach-Bliley Act on insurance companies' survival. The events of interest are bankruptcy and acquisition, which are correlated and censor each other. A statistical survival analysis method is developed first and then applied to the real insurance companies' survival data. In the methodology development, we first assess the effect of assuming independent censoring on the regression parameter estimates in Cox proportional hazard model. Then we apply the copula function to model the dependent censoring. Next, we propose an extended partial likelihood function maximized with an iteration algorithm to estimate the regression parameters and to derive the marginal survival functions under a dependent censoring setting. Simulations are conducted to demonstrate the method's performance, and sensitivity analyses are performed to assess the impact of the dependent censoring on the regression parameter estimates. In the last part of methodology, we propose a method to test the existence and to identify the location of a change-point in a hazard function. The application of our methodology to real insurance companies' survival data discloses important influence of the GLB Act on insurance companies survival.