Cookies managing
We use cookies to provide the best site experience.
Cookies managing
Cookie Settings
Cookies necessary for the correct operation of the site are always enabled.
Other cookies are configurable.
Essential cookies
Always On. These cookies are essential so that you can use the website and use its functions. They cannot be turned off. They're set in response to requests made by you, such as setting your privacy preferences, logging in or filling in forms.
Analytics cookies
Disabled
These cookies collect information to help us understand how our Websites are being used or how effective our marketing campaigns are, or to help us customise our Websites for you. See a list of the analytics cookies we use here.
Advertising cookies
Disabled
These cookies provide advertising companies with information about your online activity to help them deliver more relevant online advertising to you or to limit how many times you see an ad. This information may be shared with other advertising companies. See a list of the advertising cookies we use here.
СROs
/
/
CROs
Modern clinical trials have been introduced back in the 20th century and gradually have become the Gold standard for evidence generation in healthcare.
So, what can we do more in this field? The answer is both simple and complicated – harness the power of digitalization of healthcare system and provide even better (ie more effective) gold standard.
Trial design review and optimization via ML algorithms based on RWD by identifying patient subpopulations and by optimizing trial endpoints
Identification of best centers/geographies for patient enrollment
Traditional usage of Real World Evidence and Data has been focused on understanding disease burden, background risk and post approval pharmacovigilance. Recent analyses identify early treatment milestones and have provided a path to patient prospectives regarding treatment.
As a result, a better management patterns emerge that in turn help clinicians to better structure future search and set optimized trial goals.
Synthetic control arms and In silico clinical trials
The synthetic control arm is created by selecting a group of patients from historical data who have similar characteristics (e.g. demographics, disease stage, comorbidities) to the patients in the experimental group. The outcomes of the synthetic control group are then compared to the outcomes of the experimental group to assess the efficacy of the treatment.
Synthetic control arm can be used in various clinical trials such as in orphan drug development, rare disease, and in situations where it is difficult to find a suitable control group. However, it's important to note that synthetic control arm are not without limitations and the validity of the results obtained from this method should be carefully considered.This concept brings us closer to In silico trials – via simulating patients and using digital twins. Which can save us up to 75% of total costs of developing and commercializing a drug, let alone time constraints for recruitment and enrollment of patients.
See also
Pharmaceutical, biotech and medical device companies
Drug discovery & beyond
Healthtech
Healthcare providers
/
/
CROs
Have a study in mind?
We are here to help you to lead the cutting edge studies and leverage RWD to answer your research questions
get in touch
Schedule a capabilities call with a member of our team today and get your personal solution
TRAVERSE
Türkiye
İçerenköy Mah. Kayışdağı Cad. Acıbadem Üniversitesi Tip Fakültesi C Blok, No: 32-36c Ataşehir/İstanbul, Türkiye
© 2024
The Netherlands
Fred. Roeskestraat 115, 1076 EE, Amsterdam, Netherlands
Explore RWD