The new article highlights the key points related to the principles for the choice of clinical study design, such as bias and variability and the way they can affect the accuracy and reliability of the results.

The Food and Drug Administration (FDA or the Agency), the US regulating authority in the sphere of healthcare products, has published a guidance document dedicated to the design considerations for pivotal clinical investigations for medical devices. The document provides additional clarifications regarding the applicable regulatory requirements, as well as recommendations to be considered by medical device manufacturers and other parties involved to ensure compliance thereto. At the same time, provisions of the guidance are non-binding, nor are intended to introduce new rules or impose new obligations. Moreover, the authority explicitly states that an alternative approach could be applied, provided such an approach is in line with existing legislation and has been agreed with the authority in advance. 


Some Principles for the Choice of Clinical Study Design: Key Points 

According to the guidance, when reviewing the applications for marketing approval for medical devices, the authority assesses the part related to pivotal clinical studies to determine whether the reasonable assurance of safety and effectiveness of the product in question is provided. The authority further acknowledges that there could be different types of studies to be applied depending on the nature of the medical device in question and specific aspects associated thereto. The applicants are also encouraged to get in touch with the authority in advance to discuss the matters related to the applications they are going to submit beforehand – such an approach is expected to facilitate and streamline the subsequent review process, and also to reduce the unneeded regulatory burden, making sure that all the important aspects are duly covered. 

The present guidance describes in detail the two main types of clinical studies: clinical outcome studies and diagnostic clinical performance studies. The authority explains the way the appropriate type of clinical study should be determined, and also provides additional clarifications regarding the study endpoints. Apart from this, the document mentions certain key considerations applicable to all types of clinical studies.  

Types of Studies 

According to the guidance, in a clinical outcome study, subjects are assigned to an intervention and then studied at planned intervals using validated assessment tools to assess clinical outcome parameters (or their validated surrogates) to determine the safety and effectiveness of the intervention. The scope of such a study could also cover the clinical performance of the device in question, however, the study will be mostly focused on the clinical outcomes. As further explained by the FDA, the investigational device covers both therapeutic and aesthetic devices. The authority also mentions that a clinical outcome study is used to evaluate a diagnostic device when the goal is to evaluate the impact of how the device’s result changes a subject’s subsequent course of treatment or management by the healthcare provided. 

Diagnostic clinical performance studies are the main type of pivotal clinical evaluation for diagnostic devices. In the course of such a study, diagnostic results are obtained but are not used in the clinical decision-making process. The assessment conducted covers the output provided by the device. 

For medical devices having both diagnostic and therapeutic functions, the authority states that such products may be assessed using a diagnostic clinical performance study and/or a clinical outcome study with diagnostic performance elements. Each of these study types is described in detail in the present guidance. 


Bias and Variability in Device Performance 

The document also addresses the matters related to the general considerations for the design of clinical studies. In this respect, the authority additionally emphasizes the importance of the accuracy and reliability of data collected in the course of a clinical study over its volume. This aspect should be taken into consideration when developing a clinical study design. 

The other aspects to be considered include bias, defined as the introduction of systematic errors from the truth. According to the document, such errors could appear in all the elements, such as the way the study participants are selected or the way the study is conducted. The authority mentions that in the case of a clinical study, bias can result in an incorrect determination of safety and effectiveness, and also distort the interpretation of study outcomes, especially in case of certain issues with the performance of the device in question. As an example, the FDA describes a situation when the choice of study design is affected by the lack of information from the exploratory stage. 

Apart from bias, the factor contributing the most to the accuracy and reliability of the study results is the sampling variability, which depends greatly on the sample size. As further explained by the FDA, on the one hand, a larger sample size provides more data so that estimates of performance have less sampling variability and hence become more precise; on the other hand, a larger sample size can also result in an analysis for a clinically insignificant outcome that demonstrates it is statistically significant. Thus, to endure the accuracy and reliability of the results, clinical studies should be designed in a proper way to show both clinical and statistical significance. The FDA also mentions that the increase in the number of device samples used in a clinical study does not always result in a corresponding improvement in the quality of the outcomes. 

In summary, the present FDA guidance describes the main principles to be followed when choosing a study design. By virtue of the guidance, the authority explains how the proper study type should be selected depending on the device in question and its intended purpose, and also highlights the key points related to the most important considerations, such as bias and variability. 


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