Clinical Trials Analysis, Monitoring, and Presentation
Johns Hopkins University via Coursera
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Overview
In this course, you’ll learn more advanced operational skills that you and your team need to run a successful clinical trial. You’ll learn about the computation of sample size and how to develop a sample size calculation that’s suitable for your trial design and outcome measures. You’ll also learn to use statistical methods to monitor your trial for safety, integrity, and efficacy. Next, you’ll learn how to report the results from your clinical trials through both journal articles and data monitoring reports. Finally, we’ll discuss the role of the analyst throughout the trial process, plus a few additional topics such as simulations and adaptive designs.
Syllabus
- Clinical Trial Sample Size
- Sample size calculation in clinical trials refers to the process for determining how large a trial needs to be in order to have a reasonable expectation of detecting a difference between groups. The end result of the sample size calculation should be an estimate of the number of observations.
- Trial Monitoring
- In this module, you’ll learn about trial monitoring, which involves statistical methods to assess a trial while it is underway. These methods are used to assess safety, integrity, efficacy, recruitment, data collection, and data quality.
- Reporting Results From Randomized Clinical Trials (RCTs)
- Skilled communication of your clinical trial results is critical to ensuring that your efforts have the intended impact. In this module, you’ll learn the best practices for reporting results in both journal publications and in data monitoring reports.
- Analyzing Trials
- Analysts play an important role throughout the trial, not just at the end. In this module, you’ll learn about the analyst’s role, including how the analyst contributes to the trial at every stage of the process.
- Advanced Topics
- In this module, you’ll learn about some advanced operational functions that should be in your trial team’s toolkit, including simulations, adaptive designs, and Bayesian statistics.
Taught by
Janet Holbrook, PhD, MPH, Elizabeth A. Sugar, PhD and David M. Shade, JD