Clinical trials

are no longer the only source of information on regulatory decision-making data and the registration of medical products and devices.

Real-World Data (RWD)

Real data from registers or electronic medical records create real data (RWE), which may supplement data from clinical trials and on their basis, it is possible to conclude about the effectiveness of therapy in real clinical practice. Real-World Evidence (RWE) is defined as clinical evidence regarding the use and potential benefits or risks of a medical product derived from Real-World Data analysis (RWD). The Guidelines describe RWD as "data on a patient's health status and / or healthcare services routinely collected from a variety of sources.
Przykłady źródeł RWD

Examples of RWD sources

Dane z elektronicznej dokumentacji medycznej
Data from electronic medical records (EHR) (e.g. THIN database - The Health Improvement Network)
Dane zebrane z innych źródeł
Data from medical registers
Dane z rejestrów medycznych
Declarations and billing information.
Dane wygenerowane przez samych pacjentów
Data collected from other sources that may provide information about your health, such as mobile devices.
Oświadczenia i dane rozliczeniowe
Data generated by the patients themselves.
In conclusion, the combination of data from clinical trials and RWE allows regulatory agencies to make current and more evidence-based decisions regarding the approval of the monitoring of medicinal products.

Benefits ofusing RWE

Niższe kosty
Lower costs
Wykrycie rzadkich zdarzeń nieporządanych
Detection of rare adverse events
Zdolność do przechwytywania dodatkowych informacji
The ability to capture additional information that may impact your health outcomes
Krótszy czas do wytworzenia danych
Shorter time to data generation
Mnień ograniczeń we włączeniu populacji
Less restrictions on the inclusion of the population
RWD sources (e.g. registries, EHR files, and other health databases) can be used as the data collection and analysis infrastructure to support many types of research projects, including, but not limited to, observational studies of a prospective and / or retrospective nature. RWDs are key to determine the long-term safety profile of a drug, the quality of life of patients, the impact of the use of a given drug on treatment standards in a given country, as well as the legitimacy of drug reimbursement.
The growing recognition of the value of RWE as complementary to clinical trial evidence offers opportunities to improve regulatory approval processes for new therapies and diagnostics for drugs and devices. The value of RWE is increasingly recognized around the world, also by institutions such as the FDA.
The key challenges for the use of RWE include improving data collection and quality, and improving the methods for analyzing the relevant data to reduce possible errors. These efforts will help experts and regulators rely on RWE to improve timely decision-making for the benefit of all stakeholders involved in the patient's treatment process.

Case study

The Polish Pharmaceutical Group asked BioStat experts to perform statistical analysis of real data. The scale of the project was remarkable. The analyzed data came from the THIN (IQVIA) database from over 100,000 anonymised patients living in Great Britain.

The client's goal was to compare the efficacy and safety of combination therapy with monotherapy in patients with arterial hypertension. At a later stage, the willingness to use the results of the analyzes as the basic supporting evidence in the process of applying for approval to introduce combination pills with 2 active substances on the British and other markets of the EU member states.

The number of analyzed variables determined the use of Big Data analysis methods. Working on the project was a complex process involving several entities (Sponsors, Database Owner, BioStat, Medical Writers, Approval Committee, Regulator).
Case study
Before starting the data set analysis process, it was necessary to define the purpose, scope of data to be analyzed, as well as the possibility of their use in practice, and to compare the effectiveness of various therapeutic options in a given indication. In practice, the first retrospective study protocol was developed with approval from the Scientific Research Committee. After accepting the protocol, the database owner trained our team and the databases were handed over. BioStat performed a number of analyzes that supplemented the content of the final report.

Considered as cutting edge in the pharmaceutical sector, the project provided a better understanding of the positive and negative effects of therapy switching and highlighted the practicality of applying such huge datasets to research.

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