CtQ Factors as Communication Vehicles Ensuring Quality Alignment Across Your Study
Using Lean Six Sigma principles Critical to Quality trees is an effective tool to identify CtQ factors based on Critical Needs. Building CtQ trees helps you to translate critical study needs and quality drivers into measurable outcomes and processes. Initially, the Lean Six Sigma approach serves as a business tool based on customer needs. Still, it is a helpful method of implementing Quality by Design in clinical research, and so my article will explain the application of CtQ trees in this setting.
Quality, a Primary Consideration
Updates to international clinical research guidance such as ICH E6 (R3) (Good Clinical Practice) and E8 (R1) (General considerations for clinical trials) refer to Critical to Quality (CtQ) factors as key components of clinical trial design and Risk-Based Quality Management (RBQM).
The current draft version of E8 lists, in Annex 3, CtQ factor examples in six areas of need:
- Protocol design
- Patient safety
- Study conduct
- Study reporting
- Third-party engagement
Now how can we apply the concept of Critical to Quality factors effectively?
Build CtQ Trees in 3 Steps
As described in Lean Six Sigma, there are three steps in creating CtQ Trees as follows:
Determine the Need – It may be helpful to ask customers directly or consult with customer service representatives or people who frequently interact with customers. This is in line with the increasing emphasis in GCP on consultation with investigators and participants.
Determine the Drivers – These are the elements that drive quality and must be present to satisfy customers. These are the Critical to Quality factors.
Create the Requirements – These are the standards that must be met to meet customers’ expectations for each driver. In the clinical trial setting, these will also help us identify critical data and processes as part of RBQM.
Know Your Customers
If we want to implement this tool in clinical research, we need to understand who our “customers” are. Ultimately patients and prescribing physicians are our customers because they will be the end-users of the products approved based on our clinical trials.
There are also, however, other customers we need to consider. For example, Regulatory Authorities will review trial procedures and processes when they inspect, and the results and conclusions from the clinical trials when assessing the Marketing Authorization Application.
In addition, business customers such as the company board, investors, and shareholders want to see clinical trials conducted as quickly and cheaply as possible while meeting the regulatory requirements to obtain marketing approval and support marketing claims.
We have to consider all these customers with their very different needs, which is why CtQ Trees can be helpful.
The way this can work in practice is outlined below in figure 1.
To illustrate the “how to build a critical to quality tree” process, we will examine an example. Here we base the Critical Needs on the GCP requirements outlined in the ICH E8 (R1) Draft document. The customer would be the regulatory authorities to whom we will submit the Marketing Authorisation Application.
Feasibility as an Example
Taking Feasibility as a critical need and the CtQ factors listed herein ICH E8 (R1) draft are Study and Site Feasibility and Participant Accrual. The example CtQ tree for these is given below in Figure 2, which uses the CtQ Tree framework developed by Cyntegrity.
The example above provides a snapshot of the process and how we can use the CtQ tree to identify critical processes and data. For instance, in the Site and Study Feasibility, the critical data for the Performance Requirement “Investigator Feedback on Draft Protocol” would be the investigator’s assessment of whether the study is feasible. The critical process would be the questionnaire or interviews used to ascertain this information.
The examples given are not exhaustive but intend to illustrate how this structural approach can support a Risk-Based Quality Management system and help implement new GCP requirements. Every trial is different, but each time this approach is used, it will become easier to implement and more refined in its effectiveness.
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