RR-Drug


to

Reporting Ratios (PRR and RoR)

The proportional reporting ratio (PRR) is a simple way to get a measure of how common an adverse event for a particular drug is compared to how common the event is in the overall database.
A PRR > 1 for a drug-event combination indicates that a greater proportion of the reports for the drug are for the event than the proportion of events in the rest of the database. For example, a PRR of 2 for a drug event combination indicates that the proportion of reports for the drug-event combination is twice the proportion of the event in the overall database

PRR = (m/n)/( (M-m)/(N-n) )
Where
m = #reports with drug and event
n = #reports with drug
M = #reports with event in database
N = #reports in database


A similar measure is the reporting odds ratio (ROR).


ROR = (m/d)/(M/D)

Where
m = #reports with drug and event
d = n-m
M = #reports with event in database
D = N-M


Often PRR analyses are stratified by various attributes of the report such as patient age, gender, report date in an effort to improve precision. Other approaches, such as Bayesian shrinkage estimates of the PRR (e.g. MGPS) are also used.

References


Guidance for Industry. Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment. Food and Drug Administration, US Department of Health and Human Services. March 2005. http://www.fda.gov/downloads/regulatoryinformation/guidances/ucm126834.pdf . Accessed Dec 2014.

Bate A, Evans, S. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol and Drug Saf 2009 Jun;18(6):427-36. doi: 10.1002/pds.1742.

Szarfman A, Tonning JM, Doraiswamy PM. Pharmacovigilance in the 21st century: new systematic tools for an old problem. Pharmacotherapy 2004 Sep;24(9):1099-104.

Dumouchel W. Bayesian data mining in large frequency tables, with an application to the FDA Spontaneous Reporting System, American Statistician 1999; 53(3):177-190.

About

This software was developed by FDA's Office Of Health Informatics (OHI) as part of the openFDA initiative.

Development Team

Taha Kass-hout, MD, MS
Creator of openFDA initiative.
Jonathan 'Jay' Levine, PhD
Software design and implementation using R and Shiny, analytical methods.
Roselie Bright, ScD, FDA OC/OCS/Office of Health Informatics
Software testing and evaluation, openFDA implementation.
Zhiheng Xu, PhD, FDA CDRH
Analytical methods, openFDA implementation.

Email the openFDA team