CVM GFI #266 Use of Real-World Data and Real-World Evidence to Support Effectiveness of New Animal Drugs | Guideline Explorer

Description

This guidance describes how CVM intends to evaluate real-world data and real-world evidence in submissions to CVM to demonstrate substantial evidence of effectiveness for new animal drug applications or a reasonable expectation of effectiveness for applications for conditional approval of a new animal drug. It also provides information about how sponsors may obtain feedback from CVM on technical issues related to the use of real-world data and real-world evidence before the submission of an application.

Scope & Applicability

Product Classes

3

New Animal Drug

The category of products covered by this guidance.

Conditionally Approved Drugs

effectiveness evaluation using observational designs

New Animal Drugs

Products subject to clinical investigation guidance.

Stakeholders

4

Sponsor

Entity responsible for submitting applications under section 524B

Project Manager

FDA staff member who advises on desk copies and coordinates presentations.

Investigator

Responsible for qualifications, training, and trial conduct; Individual responsible for trial conduct and data governance at a site.; May delegate tasks but retains overall responsibility; Person responsible for the conduct of the clinical trial at a trial site; Responsible for trial conduct and participant safety; Responsible for trial conduct, data integrity, and investigational product management.; Individual responsible for trial conduct at a site and informing the institution.; maintaining

Veterinary Clinician

verifying data against their original source (e.g., veterinary clinician notes)

Regulatory Context

Attributes

5

RWD relevance

Suitability of data sources for study questions

Provenance

Provenance and transparency of data processing are established through QC/QA

Completeness

Data quality characteristic to be evaluated

Accuracy

Performance characteristic assessed via linearity experiment

RWD reliability

Quality and dependability of real-world data

Identified Hazards

Hazards

5

Recall Bias

bias prone in follow-up contacts

Information bias

Systematic distortions in the data arising from measurement error

Confounding

A factor to be managed in the analysis of real-world data; considering sources of potential bias and confounding; Studies must address two sources of error: systematic error (bias, confounding).

Bias

mitigate potential unwanted bias in learning or performance estimation

Selection Bias

A type of bias to be addressed in the study design; Evaluation of any potential biases such as information bias and selection bias.

Related CFR Sections (3)

21CFR514.5§ 514.5 Presubmission conferences.
(a) General principle underlying the conduct of a presubmission conference. The general principle underlying the conduct of any presubmission conference is that there should be candid, full, and open communication.Read full regulation →
21CFR514.117§ 514.117 Adequate and well-controlled studies.
(a) Purpose. The primary purpose of conducting adequate and well-controlled studies of a new animal drug is to distinguish the effect of the new animal drug from other influences, such as spontaneous change in the course of the disease, normal animal production performance, or biased observation. OnRead full regulation →
21CFR514.4§ 514.4 Substantial evidence.
(a) Definition of substantial evidence. Substantial evidence means evidence consisting of one or more adequate and well-controlled studies, such as a study in a target species, study in laboratory animals, field study, bioequivalence study, or an in vitro study, on the basis of which it could fairlyRead full regulation →