Structural Nested Models for Assessing the Safety and Effectiveness of Generic Drugs

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Project Summary 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 is 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 is 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.