In this course, you’ll learn to collect and care for the data gathered during your trial and how to prevent mistakes and errors through quality assurance practices. Clinical trials generate an enormous amount of data, so you and your team must plan carefully by choosing the right collection instruments, systems, and measures to protect the integrity of your trial data. You’ll learn how to assemble, clean, and de-identify your datasets.
Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.
Finally, you’ll learn to find and correct deficiencies through performance monitoring, manage treatment interventions, and implement quality assurance protocols.
Course 2 of 4 in the Clinical Trials Operations Specialization.
What You Will Learn
- Collect and manage clinical trials data
- Assemble and share clinical trials data
- Conduct statistical performance monitoring
- Perform quality assurance for clinical trials
Syllabus
WEEK 1
Data Collection Instruments
This module covers the design and organization of data collection instruments to be used in a clinical trial. A well-designed data collection instrument is critically important to the success of a trial because it determines the way that the data are defined, collected, and organized. A study without a well-designed data collection instrument is likely to encounter otherwise avoidable problems.
WEEK 2
Data Management
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.
WEEK 3
Data Assembly and Distribution
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.
Performance Monitoring
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.
WEEK 4
Intervention Management
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.
Quality Assurance
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.
