Pharmacovigilance data assessment

Statistical reasoning and algorithms in signal detection

After completing this module, you will be able to describe how statistical reasoning and algorithms can add value in pharmacovigilance, explain the idea of ​​disproportionality analysis, and calculate different measures of disproportionality. 
To enrol in this course, you must meet the following requirements:
      • Duration: 45 minutes
      • Target audience: WHO PIDM staff
      • Language: English
      • Award: Yes
      • Course level: Introductory
      • Course type: Self-paced

What you will learn

1. Statistical reasoning and algorithms in pharmacovigilance

After the session you will be able to:
  • outline at least two ways in which statistical reasoning and algorithms can bring value to pharmacovigilance

2. Disproportionality

After the session you will be able to: 
  • explain the idea of disproportionality

3. Relative reporting ratio (RRR)

After the session you will be able to: 
  • compute the expected number of reports and the relative reporting ratio (RRR)

4. PRR, RR and ROR. What's the difference?

After the session you will be able to: 
  • compute the proportional reporting ratio (PRR) and explain how it differs from the relative reporting ratio (RRR)
  • compute the reporting odds ratio (ROR)

5. Random variability

After the session you will be able to: 
  • explain the issue of random variability for disproportionality analysis, and its main consequence
  • compute the Information Component (IC), and explain its relationship to the relative reporting ratio (RR)
  • interpret IC values and uncertainty intervals, and to explain the purpose and effect of statistical shrinkage in IC values

6. Decision rules

After the session you will be able to: 
  • understand that each disproportionality measure can be used in practice with several different decision rules, and explain the purpose of these decision rules
  • recall that the proportional reporting ratio (PRR) and the reporting odds ratio (ROR) don't use shrinkage, and the practical consequence of this

7. Aspects of strength of evidence

After the session you will be able to: 
  • understand that disproportionality analysis is not the only way that statistical reasoning and algorithms can be useful in signal detection
  • recall two specific examples of strength-of-evidence aspects other than disproportionality analysis

8. A cautionary note

After the session you will be able to: 
  • recall that statistical reasoning can benefit everyone whereas statistical algorithms bring value in settings with more reports than can be manually reviewed and surveyed
  • recall that disproportionality analysis is based on a very simple model and that its results must be interpreted with caution

Course statistics

Very good course with informative explanation.
ANONYMOUS LEARNER
70% considered the content of this course to be very useful for their daily work.
62% rated the course as excellent.

Questions and answers

What is the estimated duration of the course?

This course takes approximately 45 minutes to complete.

Will I receive an award?

Students who successfully complete the course will receive a completion confirmation.

Will this course give me academic credits?

No, UMC is not an academic institution.

Where can I go if I have any problems or additional questions?

Please contact us here

Explore our other courses