Regulatory authorities have encouraged the usage of a risk-based monitoring (RBM) system in clinical trials. In addition to the identification of possible risks, risk-based monitoring also includes their evaluation to enable targeted monitoring. Risks are defined as conditions that could affect patient safety and the integrity of the study. Various studies demonstrated the increasing usage of RBM in practice. The application of the many RBM tools available has not been investigated. Central statistical monitoring (CSM) which falls under the remote monitoring of the RBM system has also been gaining more attention due to the recognition of its efficiency in monitoring clinical trials. This dissertation is dedicated to improving the quality assessments in risk-based monitoring and central statistical monitoring. The first chapter of the thesis provides an overview of clinical research and the types of clinical studies. Furthermore, it specifically focuses on clinical research structure, management, and activities in clinical trials. The different types of clinical trials are illustrated, followed by the management process of the trial and monitoring activities. Section 2.1 highlights the limitations of the current RBM tools. It shows how different an outcome risk assessment of a clinical trial can be when assessed with different RBM tools. Furthermore, this section shows the different risks covered within RBM tools. It shows the need for a risk assessment tool that can cover any risk in a clinical trial. Hence section 2.3 proposes a new risk methodology assessment (RMA) that can be applied to any clinical trial with the ability to add additional risks to the assessment. It presents a scoring method that allows stakeholders to visualize and quantify a risk size. This would guide stakeholders and assist them in the decision plan for mitigating a certain risk by an effective measure and monitoring degree in the monitoring plan. The theoretical RMA approach is presented in a shiny web app with a user-friendly interface to ease its implementation in practice. Section 2.4 proposes a new approach for the benefit of CSM. It presents multiple comparisons of individual center means to the Grand Mean of all centers. The approach is available and has been applied in different contexts. Here its implementation to detect a deviating center is recommended. As it is available for different data types, it shows specifically the comparison for continuous, binomial, and ordinal data types. In a Monte-Carlo simulation study, different model types estimating GM comparisons were tested for the control of Type I error and the highest power for balanced scenarios and unbalanced scenarios observed in clinical trials and observational studies. It also shows the validation of the approach on Real-world data (RWD) from the German Multiple Sclerosis Registry (GMSR). Finally, the approach is presented in shiny web apps to facilitate a common graphical conclusion style for different endpoints.