Real World Evidence (RWE) is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of Real World Data (RWD). It provides insights into how a product performs in real-world settings, outside of controlled clinical trials, offering a comprehensive view of effectiveness and safety among diverse patient populations.
Functional Service Provider is a specialized services designed to enhance the efficiency and effectiveness of clinical development. They offer a flexible mix of expertise, resource management, and technology to support various aspects of clinical trials and research. These services are offered to Sponsors from Pharma, Biotech, Biopharma and Clinical Research sector.
Biometrics in Clinical Trials encompass collection, analysis, and interpretation of data in clinical trials. They play a pivotal role in ensuring the quality, integrity, and accuracy of data. At Maxis Clinical Sciences, our comprehensive services include Clinical Data Management, Electronic Data Capture, Quality Control, Data Integration, Reporting, and more.
Digital Health encompass a comprehensive range of technologies including mobile applications, wearable health devices, and smart drug delivery systems designed to enhance the healthcare experience. They facilitate a more informed, engaged patient involvement in their own care and provide healthcare providers with advanced tools for better patient management.
External Control Arms or Synthetic Control Arms are comparator groups in clinical trials that are created using real-world data (RWD) from sources such as electronic health records, claims databases, and disease registries. They serve as an alternative to traditional control groups in clinical studies, particularly when randomization to a control arm may be challenging or ethically questionable.
The creation of SCAs typically involves several steps:
Common sources of real-world data for SCAs include:
Electronic Health Records (EHRs)
Insurance claims databases
Disease-specific registries
National health databases
Patient-reported outcome measures
Some potential advantages of SCAs include:
Reducing the number of patients needed for the control group
Potentially shortening trial duration
Allowing more patients to receive the experimental treatment
Providing real-world context to clinical trial results
Enabling trials in rare diseases or emergency situations where control groups may be unfeasible
Key challenges include:
Ensuring data quality and completeness in real-world data sources
Addressing potential biases in the data
Achieving appropriate matching between the synthetic and treatment arms
Handling missing data
Meeting regulatory requirements for acceptability of SCAs
Regulatory agencies such as the FDA and EMA are increasingly open to the use of SCAs, particularly in certain situations like rare disease trials or where randomization is challenging. However, they require rigorous methodology, high-quality data, and thorough documentation. Agencies have issued guidance documents outlining their expectations for the use of real-world evidence in clinical trials, including as synthetic control arms.
Refer: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-design-and-conduct-externally-controlled-trials-drug-and-biological-products
Common statistical methods include:
Propensity score matching
Inverse probability of treatment weighting
Doubly robust estimation
Bayesian hierarchical modeling
Machine learning approaches for patient matching and outcome prediction
SCAs may be particularly valuable in:
Rare disease trials where patient recruitment is challenging
Oncology trials, especially for aggressive cancers
Situations where randomization to a placebo or standard of care may be unethical
Studies of emergency interventions (e.g., pandemic situations)
Long-term safety studies
Validity assessment typically involves:
Comparing baseline characteristics between the SCA and the treatment arm
Conducting sensitivity analyses to test the robustness of results
Assessing the plausibility of outcomes in the SCA compared to historical data
Evaluating the completeness and quality of the underlying real-world data
Potentially validating the SCA approach using data from completed randomized controlled trials
Important limitations to consider include:
Potential for unmeasured confounding factors
Challenges in accounting for differences in care patterns across different healthcare settings
Difficulty in blinding investigators to the nature of the control arm
Potential skepticism from some stakeholders about the reliability of non-randomized comparisons
Limitations in available real-world data for certain outcomes or patient populations