The U.S. Food and Drug Administration (FDA) expects that approved generic products provide the same quality, safety, and efficacy as the corresponding brand. Despite this, some clinicians and patients are reluctant to use generic medications due to fears of lesser effectiveness or concerns about toxicities or side effects. The FDA seeks to ensure that patients can confidently access generic drugs, and that substandard products be removed from market. This requires appropriate pre-marketing regulation and post-marketing surveillance to understand generic drugs? clinical effects. We propose methods to enhance the FDA?s ability to evaluate the safety and effectiveness of generic drugs relative to their branded counterparts using healthcare utilization database (claims data), and electronic medical records (EMR). There are major challenges in making valid causal inferences regarding the comparative effectiveness of generics and branded drugs using these secondary sources of data. Some of the key challenges are: misclassification of outcomes, missing key variables for confounder adjustment, and data that are potentially informatively missing due to patient losses to follow up.
Our first aim i s to develop a rigorous, state-of-art, causal inference approach for comparing the toxicity and efficacy of generics and branded therapeutics that will be applicable to claims databases.
Our second aim i s to leverage the EMR data linked to claims, to enhance the methods developed in Aim 1. In the third aim, we propose to train the FDA scientists from the Office of Generic Drugs (OGD) in implementing our methods. We propose to apply o ur approach to the study of commonly used drugs in breast cancer: aromatase inhibitors, for which generics are available. We will use a linked data set from Optum Labs which includes both claims data and EHR data. This database has in excess of 150 million individual patient records for claims and 30 million patients for claims-EMR covering 10 years or more of patient experience.
Specific Aim 1. To develop a state-of-art causal inference approach for comparing the toxicity and efficacy of generics and branded drugs that will be applicable to healthcare utilization (claims) databases.
Specific Aim 2. To demonstrate the added value of linked claim-EMR data for surveillance of generic drug effectiveness and safety.
Specific Aim 3. To provide training to the FDA scientists from OGD in implementing our methodological approach.

Public Health Relevance

Generic drugs are cheaper than brand-name drugs. However, there is general reluctance among patients to take generics because of the perception that they may not be as safe and effective as brand-name drugs. This project will develop tools for statistical analysis which can help determine, in a reliable maner, whether the generic version of drugs are as safe and effective as the brand-name drugs.

Agency
National Institute of Health (NIH)
Institute
Food and Drug Administration (FDA)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01FD005556-01
Application #
9106268
Study Section
Special Emphasis Panel (ZFD1-SRC (99))
Project Start
2015-09-15
Project End
2018-08-31
Budget Start
2015-09-15
Budget End
2016-08-31
Support Year
1
Fiscal Year
2015
Total Cost
$199,999
Indirect Cost
Name
Johns Hopkins University
Department
Type
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205