By Matthew Cardinal and Lily Borisov, Halloran Consulting Group, Inc.
The drug development industry’s focus on the discovery and therapeutic aspects of precision and genetic medicine, as well as the continued need for virtual, remote patient visits, has highlighted the fundamental role that individual patients have in the life cycle of development. As the industry continues to shift toward making a patient-centric approach the standard, the FDA is also releasing new guidance documents to help influence these changes. This past month, the FDA released one of what is to be four new guidances providing a general overview of patient-focused drug development. This new guidance, titled Guidance 1: Collecting Comprehensive and Representative Input, is primarily centered on how to collect patient experience data in patient-focused drug development. The purpose of this article is to summarize each component of Guidance 1 to highlight some of the main points addressed in the guidance and detail how the guidance may impact sponsors of clinical trials.
Understanding Patient Experience Data
The purpose of this new FDA guidance is to address the requirements set forth in the 21st Century Cures Act of 2016 — a set of regulations intended to help accelerate medical product development. According to the FDA guidance, The Cures Act defines patient experience data as “data that is collected by any person with the intention to provide information about patients’ experiences with a disease or condition, including the impact of the disease or condition or related therapy or clinical investigation, and patient preferences with respect to treatment of the disease or condition.”1 This broad definition of patient experience data allows sponsors to use a variety of different types of data sources to collect this information but may also make it difficult for sponsors to ensure that the right data is being collected from the right population. The new FDA guidance provides an outline of how to ensure the right study data is collected, from the right source population, to adequately answer the research question of interest.
Sponsors need to understand what patient experience data is and how to collect it. It is essential to implement data collection methods accurately and in a compliant manner, as they will affect the validity of the results in a clinical trial, ultimately influencing the interpretation of the effectiveness of the final medical product and its relationship to the patient experience. Patient experience data contains an abundance of information pertaining to patients’ experiences living with the disease or condition of interest. Examples of this include, but are not limited to, the impact the condition has on patients during their day to day lives, their experiences with current treatment options, and how patients weigh the risks and benefits of each treatment option available to them. Keeping the patients’ insights in mind and proactively working to collect accurate data on patient experience will help the sponsor create a more successful medical product with more value to patients. In some instances, patient experience data and the respective data collection tools can help accelerate the development process for sponsors, as their trials are now obtaining information directly from the medical product’s end user: the patient.
Designing A Study To Collect Patient Experience Data
Two of the most important fundamental concepts when designing a trial are 1) clearly defining a research question and 2) defining the source population that will be utilized to answer this research question. In instances where a broad research question is used, the FDA suggests dividing up the research question into separate detailed study objectives to help with enhancing the validity of the study results. For example, the study question may be to understand patients’ overall experience with a certain treatment regimen, which can further be defined as how burdensome the treatment regime is to the patient and, due to the nature of the patients’ symptoms, how this impacts their adherence to the treatment regimen. When sponsors are developing and defining a research question, they should also consider which methods are better suited to answer the question and how the study materials should be designed to adequately answer this question.
After the study question has been clearly defined, the source population from which the study sample will be derived needs to be clearly defined. Since it is not feasible to study all members of a population due to cost and time, a representative study sample needs to be derived from the source population for the study results to be generalizable. Sponsors need to focus on capturing this data accurately to ensure that the medical product’s label will include the most relevant and useful information possible. To properly capture the study population from the correct source population, sponsors must carefully consider the study’s inclusion and exclusion criteria. While designing the inclusion and exclusion criteria, some characteristics need to be taken into careful consideration, including whether the intention of the study is to examine newly diagnosed patients versus all patients diagnosed with a certain condition, as well as certain age groups, or patients from a certain demographic background. For example, a sponsor may seek patient experience data to inform a prospective study design or protocol for a trial, and thus the population should reflect the patients that may be included in such study. Another important consideration when choosing the source population is whether it may be fixed or dynamic, e.g., can recovered patients be a part of the study sample or only patients currently affected by the disease of interest? Correctly identifying the source population will ultimately deem whether the study question is answered successfully and if a successful product is developed.
Carefully consider how the source population can provide their experience in the most effective and reliable means possible. Although data reported directly from patients is the recommended approach, due to the source population chosen, this may not always be the best approach to obtain reliable and accurate data. Another consideration is to understand how the disease progresses over time; initially, the patient may be able to report directly on their own, but as time progresses it is important to plan ahead if other methods of obtaining data will be needed.
Many of the items related to study design outlined in the FDA guidance are examples of reducing systematic error. When designing the study, take into consideration whether an observational or experimental study will be better suited to answer the defined research question. Part of choosing the appropriate study design is also choosing the appropriate sampling method, ensuring your study sample is representative of the source population, and ensuring you have an adequate sample size. The FDA categorizes sampling methods into two categories: 1) probability sampling and 2) nonprobability sampling. Whenever possible, probability sampling should be used because it helps to ensure that the study sample is representative of the source population. The FDA suggests that the use of a sampling frame can also help with probability sampling and ensuring your study sample is generalizable. A sampling frame is the source material that a study sample can be drawn from; examples include hospital or national disease registries.
Once the study has been designed, it is important to do a final review of the study protocol and any plans associated with the study, using external reviewers. This will help to ensure that the study design has all the relevant tools and information needed to efficiently collect the right patient experience data for the specific study question. The external reviewers can also determine if it is feasible to perform the study. The external experts brought in to review the study can be organized into a Scientific Review Committee that will be responsible for not only reviewing study materials but also to critically examine procedures during study conduct.
Data Collection And Data Management
It is not only important to collect the right patient experience data, but the data must also meet agency and regulatory quality requirements. The FDA recommends standardizing data collection methods to ensure the study has high-quality data. Some key elements of high-quality data include a diverse selection of sites, protection of human subject information, data confidentiality, and having a defined sampling strategy.
A part of the sampling strategy is clearly defining how data will be collected and understanding the type of data each collection method will produce. When choosing data collection methods, consider which methods are most appropriate for the study question and chosen source population. The FDA guidance lists a variety of sampling methods that can be used to ensure collection of reliable and accurate data, including observing patients, having patients complete surveys, or interviewing patients. The list provided by the FDA provides evidence of the FDA’s commitment to alternative research methods promoting the collection of patient data virtually and reducing the burden on patients when collecting critical study data. This gives sponsors the flexibility to collect data from patients outside of the standard course of running a traditional clinical trial. When interviewing patients, it is important to consider if group or individual interviews will be more valuable, depending on the research question. It is equally important to spend a good portion of time training the interviewers to improve the consistency of the research. Unconscious bias and tone of voice from the interviewer can sway the interviewee’s responses.
This newly released guidance expands upon the standards we are already using in clinical trials to allow more flexibility in collecting patient data and reducing overall patient burden. This guidance and other new guidances released by the FDA continue to outline ways stakeholders can creatively design their trials to minimize the burden for the patient, all while acknowledging that there is a set of minimum standards that must be met. It is clear from this guidance that the FDA stresses the importance of reducing patient burden in a trial, beginning with designing a well-vetted trial that includes patient input in the process. The concepts described not only apply when gathering patient experience data but throughout the entire clinical development process and should be applied to all new studies moving forward. The FDA’s commitment to patient-focused drug development via this new guidance is an important step to advancing patient-centered designs and evaluations.
About The Authors:
Matthew Cardinal has more than 15 years of experience in biologics, drug, and medical device development. At Halloran, he functions as a team leader in the design, planning, and management of development strategies for hematology, immunology, gene therapy, regenerative medicine, and infectious disease/vaccine programs. Cardinal supports all development lifecycles, from pre-IND through registration, with a focus on translational clinical research and quantitative drug development. He has a specific background and interest in indications for rare diseases and AAV gene therapy, including hematology and rare immunodeficiencies. Prior to joining Halloran, Cardinal was a clinical development lead for early phase rare disease programs at Pfizer.
Lily Borisov, a consultant and project associate at Halloran Consulting Group, has clinical trial experience in diagnostic development and has developed skills in clinical trial design, quality, project management, literature reviews, EDC, database management, and data analysis using SAS. Her responsibilities include establishing project timelines, supporting subject matter experts on specific projects, identifying opportunities for project expansion, and assisting with devising support strategy. Previously, Borisov worked as a senior clinical research coordinator at a biotechnology start-up, where she was involved in both study start-up and conduct. In this role, she worked interdepartmentally and helped develop tools, systems, and workflows to drive optimal clinical trial execution.