As I mentioned in my previous post, the FDA is engaged in a broad effort called CFAST (The Coalition for Accelerating Standards and Therapies) to standardize “research concepts” for use in clinical trials for various therapeutic areas. This has led to the idea of a “Therapeutic Area (TA) Standard.” What exactly is a TA Standard? Here are my personal thoughts on this important topic.
“Standard” vs. “Data Standard”
When discussing TA standards, it’s useful to draw a distinction between a standard and a data standard. A standard is defined in dictionary.com as “something considered by an authority or by general consent as a basis of comparison; an approved model.” There are many different kinds of standards: manufacturing standards, measurement standards, data standards, etc.
To understand the distinction, consider a ruler. The ruler can be marked in inches or centimeters. Which ruler one uses to measure length depends on the measurement standard that one has selected for the task. Once one selects a measurement standard, then the data standard provides a consistent approach to document and share the measurement. If the measurement standard is inches, and the measurement is 10 inches, then the data standard describes whether it’s 10”, 10 in, or 10 inches.
The distinction between a standard and a data standard is important when considering TA standards. How to represent a measurement (i.e. an observation) requires two decisions: a business decision (what to measure, which measurement standard to use), followed by a data standards decision (how to standardize the representation of the measurement, which data standard(s) to use).
What is a Therapeutic Area Standard?
There is no widely established “standard definition” for a TA standard. One working (perhaps prevailing?) definition is that a TA standard is a data standard for a therapeutic area or indication. However, close inspection indicates that a TA standard is not a data standard. Let’s examine the definition of a TA standard more closely.
Let’s consider a clinical observation, specifically a clinical laboratory test: hemoglobin A1C (HbA1C). The standardization of HbA1C data is straightforward. CDISC provides controlled terminology for the HbA1C lab test (represented by the NCI EVS code C64849). The CDISC SDTM IG describes how to represent lab test data (which includes HbA1C data) using the LB domain. The result is a numeric value, and CDISC terminology provides standard terms for units of measure. Anyone conducting a clinical trial that includes the collection of HbA1C need only look at the SDTM IG and CDISC controlled terminology to understand how to standardize this information. No additional data standards are needed.
Let’s now consider a single therapeutic area: Diabetes Mellitus. Let’s assume that, for the purpose of determining efficacy of a new diabetes drug, only one outcome measure is necessary: the HbA1C. So what does a Diabetes Mellitus TA Standard then look like? What are we “standardizing” that isn’t already standardized?
One can envision a separate Diabetes TA Standards document that says, “if you’re studying a new drug to treat diabetes, you should collect HbA1C and here is how you should represent HbA1C data using these existing standards: SDTM + CDISC controlled terminology.” For this document to be truly useful, an independent scientific and/or regulatory body should first decide what design features and clinical observations are relevant for diabetes studies. This can be described as a “good clinical research practice guideline” for diabetes. One could consider this a standard but it is not a data standard. Such a guideline is analogous to a manufacturing or building standard. Just as a builder might say: “A hurricane-resistant building must/should contain these materials: ….,” a clinical researcher would say: “A good diabetes study must/should contain HbA1C testing.”
The FDA publishes such guidelines. These are called indication-specific guidances. During my time at FDA, I was the principal author of the template used to standardize the format and content of indication-specific guidances that CDER issues. These guidances help sponsors design their development programs, including the pivotal clinical trials, to support U.S. approval of new drugs for a given indication. Other organizations may publish similar guidelines: professional societies, other government agencies (e.g. NIH), consortia, etc.
In this simple example, a researcher would only need to read the clinical research guideline for diabetes, and understand how to represent HbA1C data using existing FDA-supported exchange and terminology standards. No additional documentation is necessary. A Diabetes TA user guide is not needed for this simple example. The “standard” for a diabetes trial is the clinical research guideline itself and the existing data standards.
Of course therapeutic areas are much more complicated than this. Each TA has multiple relevant clinical observations, and the observations themselves have additional metadata needed to interpret the observation. In this setting a TA “user guide” is useful to demonstrate how to represent all TA-relevant data and metadata using existing data standards. But the user guide itself is not a data standard. The data standards are the exchange and terminology standards that the user guide references.
So an alternative definition for a TA Standard is a best practice guideline for conducting clinical trials for a specific therapeutic area, with an accompanying illustration (“user guide”; TA “use case”) on how to use existing data standards (exchange and terminology standards) for that TA. If CDER generates the guideline, then it would be an indication-specific guidance, and any available user guide would ideally be incorporated by reference to the guidance.
An analogy would be a best practice specification for a kitchen. The kitchen “standard” would say: it must have cabinets, a refrigerator, a sink and faucet, and a stove and oven. It may have a garbage disposal, dishwasher, and trash compactor. The kitchen TA user guide might say, “this is what your kitchen would look like if you use standard Ikea cabinets and General Electric appliances.”
How should TA Standards Be Managed?
First we should recognize that a TA standard is not a data standard. It is a use case for data standards. The data standards are the exchange and terminology standards that are used to standardize TA-specific data. A TA standard has two components:
- A clinical research ‘best research practice guideline’ or standard (i.e. the data requirements)
- A description of how existing data exchange and terminology standards can represent the TA data requirements. A user guide may be useful (but not necessary) to illustrate how this is done.
In the trivial example described here, there is really no need for a separate Diabetes TA user guide because it is clear how to represent HbA1C information using existing data standards. The problem arises when the data requirements are complex and the existing data standards do not provide a clear and unambiguous representation of the clinical data. Then, a user guide is helpful and necessary.
For a TA standard to be effective, they must meet FDA’s regulatory needs. We need a process to ensure that:
- The TA best practice research guideline is accurate (the TA-specific data requirements). From FDA’s perspective, this is ideally captured in an indication-specific guidance, and
- Data standards exist (or have been adequately modified) to represent the data requirements in a standard format