The US Food and Drug Administration (FDA) recently finalized a guidance document outlining the agency’s thinking on Submitting Patient-Reported Outcome Data Cancer clinical Trials. The guidance illustrates the importance of choosing a PRO vendor that is capable of capturing the recommended content and framework as source data if possible. This will reduce the need for time-consuming and potentially error prone data manipulation in SDTM and ADaM.
The US Food and Drug Administration (FDA) recently finalized a guidance document outlining the agency’s thinking on Submitting Clinical Trial Datasets and Documentation for Clinical Outcome Assessments Using Item Response Theory. The guidance lays out technical specifications to consider when submitting clinical outcome assessment (COA) information that uses Item Response Theory (IRT) in a marketing application.
Are you developing a new product and wondering whether it falls under the classification of a drug or a medical device? While some products clearly fit in one classification or the other, with advancements in science and technology, products can have features that blur the lines between drug and device. Navigating the regulatory landscape is often a complex but critical aspect of bringing your innovation to market. In this series, we will explore the regulatory mechanisms available to help you distinguish a medical device from a drug product and to utilize FDA’s regulatory pathways specific to each, particularly in early development.
The Prescription Drug and User Fee Act (PDUFA), first enacted in 1992, was put in place to ensure timely FDA approval or licensure decisions of New Drug Applications (NDAs) and Biologics License Applications (BLAs). To streamline this decision-making process, the FDA developed a structured benefit-risk assessment framework designed to highlight uncertainties about a drug’s safety and efficacy in the indicated population and where new findings, as they become available in the post-market setting, can be incorporated into the framework.
Seasonal affective disorder (SAD), also known as major depressive disorder with a seasonal pattern, is a condition of regularly recurring depressive episodes that most commonly occur in autumn and winter and remit during spring and summer. Despite the relatively high prevalence and morbidity of SAD, this disorder still often goes undiagnosed, in large part because health care professionals do not generally consider a potential seasonal pattern in a patient who presents with depression.
Since the introduction of estimands, the answer to “Why are estimands necessary?” has remained constant – to allow for clearer communication about benefits/risks of a potential treatment to the relevant stakeholders. However, as illustrated in this blog, the number of questions as to how & when to use them has grown considerably.
Analyses conducted using the overall population can mask the extent of the benefit-risk in subgroups of patients. Thus, subgroup analyses are critical to fully understand the benefit-risk in pivotal oncology trials and guide regulatory decisions for approval and labelling. However, subgroup analyses also present several challenges especially for overall survival (OS) where larger sample sizes and longer follow-up are needed, and where early analyses of immature data may be conducted.
Real-World Data (RWD) is being increasingly utilized in clinical research to help support drug approvals. The U.S. Food and Drug Administration (FDA) guidance on Considerations for the Use of Real-World Data and Real-World Evidence (RWE) to Support Regulatory Decision-Making for Drug and Biological Products provides thoughtful recommendations on the use of RWE to support approval of a new indication for marketed drugs. This guidance is part of the FDAs Real-World Evidence Program which provides additional guidance for the use of RWE to support regulatory decisions.
How do we get both information to rule out harm and assess the overall survival benefit, while still being realistic in our conduct of oncology clinical trials? The answer to that question, depends on a variety of factors, such as the type and aggressiveness of the cancer. As you are designing your next clinical trial assessing OS, we have listed some items to keep in mind.
