In this module, you’ll learn about data management in the context of clinical trials. You’ll learn definitions and core concepts and explore a few different frequently used data management systems. We’ll look closely at Excel and other spreadsheet programs because they are widely used and help illustrate broader data management principles. You'll also learn about data integrity, which incorporates features of data security, redundancy, and preservation.

Data assembly involves preparing data for distribution to others. In this module, you’ll learn the necessary steps for creating datasets for sharing. We’ll cover data freezes and data locking as well as cleaning, de-identification, sharing, and standards that you and your team can use to make your data more useful.

In this module, you’ll learn how to conduct performance monitoring in clinical trials. Specifically, we’ll discuss a framework for monitoring clinical center performance and protocol adherence through all phases of the trial from start-up through follow-up. The module will conclude with a brief overview of site visits, an important part of a performance monitoring toolkit.

In this module, you’ll learn about the principles of managing treatment interventions. There’s a considerable amount of heterogeneity in clinical trials, so a number of factors can influence how you deal with the intervention. Factors include the hypothesis, the design, whether it is an improved intervention, and whether it is licensed or experimental. You’ll also learn about different types of drug formulations and how they factor into masking protocols.

In this module, you’ll learn about quality assurance, which refers to the various measures that you and your team can take to help prevent mistakes or problems in your clinical trial. These measures can differ throughout the stages of the trial, so we’ll discuss the specific context in which these measures should be used.