I’m Gilda Teissier, Marketing Director at EvidentIQ and I recently met with Thomas Verjus, Global Business Developer at Carenity, part of the EvidentIQ Group.

Decentralized clinical trials (DCT) are one of the latest clinical research innovations and are defined by Mckinsey as studies “centered around patient needs that improve the patient experience. The focus of such a trial is on making it more convenient, closer to the patients, or both by using a combination of virtual and physical elements to conduct the required trial procedures.”

The COVID-19 pandemic accelerated dramatically the adoption of DCT. In this context, many health regulators (FDA, EMA, MHRA,…) adopted urgent and temporary guidance to allow sponsors to adapt their current protocol and be able to conduct clinical trials remotely. But as time passes, all these regulators have realized that the use of “virtual elements” has grown in parallel with acceptance of “virtual medicine,” accelerating shifts in clinical trial design that many feel are long overdue. The shift has been reinforced by growing digital health technologies and also a rising interest in patient centricity from sponsors.

The following interview will give us an overview on the current DCT landscape and their importance, as well as an insight on the EvidentIQ Way.

Gilda: Why are DCT important?

Thomas: Because they help bring new treatments faster on the market and make it easier to reach demographics which would not have been thought attainable before. In a fully decentralized clinical trial, there is a minimum or even no need to go to a study center, which can accelerate the process and give more options on a pandemic, or not, context. Comparing to traditional clinical research, DCT will allow to speed up patient enrollment, increase patient retention and collect better quality of evidence thanks to remote data collection.

Gilda: How are DCT changing the clinical trial landscape?

Thomas: A few clinical trials were already using some virtual components before 2019. DCT have become a growing market since the COVID-19 pandemic. Recent first guidance published by health authorities are also encouraging a rising DCT adoption. In 2022, it is expected that there will be around 1,300 decentralized trials and/or with a virtual component. This will mean an increase of 28% compared to 2021. So, it is pretty clear that if you want to conduct a successful trial this year, DCT is your answer as it appears to be the new gold standard for the industry.

“In 2022, it is expected that there will be around 1,300 decentralized trials and/or with a virtual component.”

Gilda: Which are the main benefits of DCT?

Thomas: There is a long list of DCT benefits, but in a nutshell, I think we can say that the first and most important is the easy access to patients wherever they live. You can conduct online patient recruitment and hence have faster patient enrollment, especially adapted for some specific diseases. This also helps to have better patient engagement and less drop-out as every step of the trial is easier for them, avoiding burden of onsite visits.

Aside from that we can also mention that DCTs have higher data quality for the sponsor, real-time monitoring, less source data verification, fewer intermediaries, a predefined and compliant format of data, among other things.

Benefits of DCT are “easy access to patients wherever they live. […] better patient engagement […] higher data quality for the sponsor…”

Gilda: Which are the challenges of conducting a DCT?

Thomas: Even though they are the future of clinical trials, there are still some issues that need to be perfected. Some conditions may prevent doing DCT even in hybrid mode, not all the population may have access to the necessary technology or might not be tech oriented. At the same time some of the exams might only be conducted at a hospital. And this is without talking about specific ethical or data privacy regulations.

Before starting a DCT, it’s key to be pragmatic here and ask the good questions to define the most adapted DCT strategy – hybrid of fully decentralized depending on virtual components to be selected.

However, despite this, DCT are worth the while and they are proving to be faster and more effective as time passes while bringing a great option to the table, a more diverse panel.

Gilda: Is it true that the costs of running a DCT are lower?

Thomas: Yes, it is, DTCs are a real game-changer, the time & costs to run a trial are substantially reduced thanks to the model. You only need to remember that every aspect of the trial needs to be thought through, so it can go smoothly and save you money.

“DTCs are a real game-changer, the time & costs to run a trial are substantially reduced thanks to the model.”

Gilda: How is EvidentIQ a key player in DCT?

Thomas: At EvidentIQ we offer a direct access to patients with a global platform of 500k patients on 1,200+ conditions to design a patient centric DCT and speed up patient enrollment. We also combine best-in-class technologies and data sciences services to offer an innovative DCT experience. We have a Hybrid or 100% digital model with fully digital CRO services and innovative data sources to optimize your clinical research.

Gilda: Thanks a lot for your time.

For more information and a deeper understanding on this topic be sure to check out our upcoming webinar on this topic which will take place on March 10th at 5pm CET.

I’m Gilda Teissier, Executive Marketing Director at EvidentIQ and I recently met with Lise Radoszycki, COO at Carenity and Global Head of Data Science at EvidentIQ.

Over the past decade, we’ve observed a rise in patients’ power and engagement regarding their own care. 

The concept of Evidence-based medicine has also emerged with the goal of delivering the right care at the right time to the right patient. One of the core concepts of Evidence-based medicine is patient preference.

Industry, regulators, HTA bodies and players are showing more and more interest in the use of patient preference studies. The goal is to help incorporate the patient perspective into clinical drug development, care management, and healthcare decision-making.

In this context, most of the pharmaceutical companies want to be patient-centric, but it’s not always clear how and when to include patient perspective into the product development cycle.

The following interview with Lise Radoszycki, COO at Carenity and Global Head of Data Science at EvidentIQ, will give us an overview of preference study methodologies focusing on Discrete Choice Experiments (DCE), and will share light on how to optimize DCE design to improve the quality on patient studies.

Gilda: First of all, could you explain to us what a preference study is?

Lise: Of course, patient preference studies are based on patient preference information, that reflects what treatment attributes matter to patients, how much these matter to them and how they make trade-offs between treatments attributes.

Describing trade-offs allows us to evaluate compromises done by patients when they select their preferred treatment option. To illustrate this concept, I will ask you 3 questions:

1. Do you prefer to be rich or to be poor? I guess that your answer will be rich. In the same way, if I ask you, do you prefer to be sick or to be healthy? Again, the answer is easy, you will mostly say “healthy.”

2. Now, if I ask you “Do you prefer to be rich and sick or poor and healthy?” The choice is more complicated, and you will need to make some compromises or trade-off.

For each of these 3 types of information, qualitative or quantitative methodologies exist. Qualitative methods are mainly based on focus groups or semi-structured interviews, whereas quantitative methods are mainly based on questionnaires administered directly to patients.

Among the quantitative methods there are two main types of methodologies: the revealed preference methods and the stated preference methods.

Unlike revealed preference techniques that use observations on real choices made by respondents, stated preference methods ask patients how much they value something. This type of methodology allows to evaluate a treatment that is not yet on the market or a drug that targets a restricted population of patients such as a rare disease or a specific disease subtype. Stated preference methods are widely used in healthcare. In this type of method, the subject is asked how much they value something and here is where the Discrete Choice Experiment comes in.

Gilda: What is a Discrete Choice Experiment?

Lise: A DCE is a stated preference method increasingly used in healthcare to elicit patient preferences. In a DCE, participants are asked to state their preferred choice between several competing treatment alternatives. In general, there are two treatment options. The features of these treatments are called attributes. Each attribute is decomposed into levels. Each treatment alternative presented to participants is a combination of treatment attributes and levels. The question asked to patients might be: “If you were offered these two treatments, which one would you choose?”

“In a DCE, participants are asked to state their preferred choice between several competing treatment alternatives.”

Gilda: And which would be the main questions that need to be asked in order to develop a successful DCE?

Lise: For me there are three questions that help create the best study design. You can use these best practices for all patient studies, not just preference studies.

The first question is: Which research question is best suited for the strategic objectives to be achieved by measuring patient preferences?

The second: Which study population would be most appropriate to answer the defined research question?

Finally: What is the most appropriate methodology to answer the research question while considering the operational constraints?

Gilda: If we take this into consideration, it would mean that the DCE is not the right method for every preference study?

Lise: Even if the DCE is considered as a gold standard, it is not always the most appropriate method. Indeed, it is important to highlight that the main guidelines on preference studies published by the NICE, the FDA or ISPOR do not impose a particular methodology, but propose criteria for assessing the quality of patient studies.

For example, if the goal is to evaluate preferences regarding several topics (Qol, care pathway) the DCE is not the most appropriate methodology. Same if you want to evaluate the perception of a specific treatment option testing some pre-defined hypothesis. It is also very important to consider the operational constraints such as the prevalence of the study population, the timelines, the budget allocated to the study, etc.

Gilda: There are 5 key steps to a preference study with DCE methodology, could you tell us more about them?

Lise: Briefly, we could say that the first step is to identify the attributes of the treatment. The second, is to identify and define the levels of each attribute with methods such as literature research, and qualitative interviews. The third step is to create scenarios and choice sets with an experimental design method, which will be used to select a reduced sample of choices (named “fractional factorial” design). The fourth step is Statistical Modeling based on the random utility model. And the final key to a DCE study is the interpretation of results, where it is crucial to identify those that have a negative or positive impact on the preference of a treatment. Patient preference studies generate insightful data that can be used at each step of the medical product life cycle. 

Gilda: Do patient preference studies really make a difference in regulatory decision-making?

Lise: This is a very interesting question. Several Authorities (such as NICE or FDA) invite companies to start a conversation with them about using patient preference information to support their submission. Over the past years, several guidelines and initiatives have been developed to implement and improve quality standards (such as PREFER or IMI).

There is therefore, a real desire to include patient studies in regulatory decision-making. Nevertheless, the level of consideration given to this type of study is very variable and highly depends on the quality of the study.

For example, Janssen included in its dossier a preference study to support Esketamine, a treatment for resistant depression. The goal of this DCE study was to assess patient perspective on benefit-risk trade-offs. In their HTA dossier, outcomes importance was reinforced by the results of the patient study. The advisory committee of the FDA said that it was taken into account in their decision, but we do not know how much influence it really had at the end.

Gilda: Before leaving, could you give us some final tips to a successful DCE study?

Lise: First, it is really important to make sure your participants fully understand the study materials to reduce uncertainty caused by health numeracy/literacy.

For this purpose, you can use a pilot study or a cognitive debriefing to pre-test the questionnaire and the material associated with the study with a limited number of patients.  

The second advice would be to ensure representativeness of the sample and generalizability of results.

Another important thing to remember is that in order to improve the quality of the results, it is crucial to minimize the potential cognitive biases, such as framing bias, anchoring bias, ordering or labelling effects or simplified heuristics.

The final advice that I can give is to think carefully about the statistical analysis plan and data management plan in order to ensure logical soundness and robustness of data analysis.

Gilda: Thank you for your time.

A webinar addressing this same topic was recently conducted by Carenity and Takeda through the Xtalks platform. If you want to watch the replay you can access it by clicking here: ACCESS WEBINAR

For more information about EvidentIQ’s and Carenity’s solutions click here.