ROLE OF BIG DATA ANALYTICS IN CLINICAL TRIALS
Introduction:
Data analytics can be readily used for clinical trial design and analysis, expanding patient selection criteria, swiftly sifting through various parameters, and helping researchers better target patients who match the criteria for exclusion and inclusion.
Big Data Analytics For Clinical Trials
Big data analytics for clinical trials provide companies the ability to process and interpret a large volume and variety of information that is accumulated at great speed. This ability is contributing to the increased use of big data and related technologies in the pharma or healthcare industry, specifically in clinical trials.
Pharmaceutical companies have long relied on data to identify patterns, test theories, and understand the efficacy of treatments. In this article, we’ll examine how big data analytics helps clinical trial researchers streamline complex processes and improve efficiency throughout clinical study development and execution.
What are the primary types of clinical trials?
Clinical research is simply the study of human volunteers, or clinical trial participants, that help doctors and researchers learn about new solutions to health problems. Data from clinical trials are then examined using big data analytics to determine whether certain therapies will continue to progress through the drug development process.
The Primary Types Of Clinical Research Are Observational And Clinical Trials.
Observational trials
One type of clinical research is observational trials, where researchers gather data from people in normal settings. No specific new drug or medical device, procedural or behavioral intervention is done with these clinical trial participants, as is done in clinical trials. For example, a clinical researcher may observe a group of older volunteers over a period of time to measure the effects of aging or lifestyle on cognitive or cardiac health.
Clinical trials
A clinical trial is a research study of humans to test the safety and effectiveness of a specific medical, surgical, or behavioral intervention. A lead investigator heads every study, usually a medical doctor, with a research team of doctors, nurses, and other HCPs. They may study new drugs or medical devices, different surgical techniques, better use of existing treatments, or behavioral changes that can improve health.
Before a clinical trial for humans is approved by the Food and Drug Administration (FDA), preclinical research must be performed in labs to answer basic questions about a new therapy’s safety. Then a clinical trial is run according to the medical research plan or protocol created by the researchers or manufacturer. The protocol addresses:
Why the study is being conducted
Who may participate in the study
Number of participants needed
How long the study should last
Schedule of tests, procedures, drugs, and their dosages
What assessments will be done, and when
What data will be collected, and how it will be analyzed
The primary, but not only, types of clinical trials include:
Screening trials test new potential advancements in detecting health conditions and diseases.
Diagnostic trials look for more effective methods, like tests and procedures, to identify certain illnesses or conditions.
Treatment trials test interventions such as treatments, new drug combinations, medical devices, and different approaches to surgeries and other therapies.
How is big data used in Clinical Trials?
Big data can be used in many ways throughout the clinical trial process. One intriguing use case is to use big data to help pharma organizations enroll more people into clinical trials – a primary concern during the process and often where clinical trials get bogged down or fail altogether.
People who are recruited to join clinical trials may become part of the experimental group – the people who receive the drug or treatment being investigated – or the control group, who receives no treatment, a placebo, or the current standard of care. But what if investigators could use all of the recruited participants as the experimental group, thereby gathering more data points? From MIT Technology Review:
“The idea is to reuse data from patients in past trials to create “external control arms.” These groups serve the same function as traditional control arms, but they can be used in settings where a control group is difficult to recruit: for an extremely rare disease, for example, or conditions such as cancer, which are imminently life-threatening. They can also be used effectively for “single-arm” trials, which make a control group impractical: for example, to measure the effectiveness of an implanted device or a surgical procedure. Perhaps their most valuable immediate use is for doing rapid preliminary trials, to evaluate whether a treatment is worth pursuing to the point of a full clinical trial.”
Pairing big data with other technology, such as that used in EDC clinical trials, can also help to accelerate the trial process and make data cleaner and more reliable, as well as more accessible to anyone who needs to use it. Big data analytics may also increase the value of clinical trial data sharing, which increases trial participants’ contributions to generalizable knowledge about human health by potentially facilitating additional findings beyond the original, prespecified clinical trial outcomes. The use of big data analytics may also have implications for improving the diversity of demographic data in clinical trials, particularly in examples like the above, where investigators can use all recruited participants as an experimental group.
While big data analytics have a number of positive implications for the future of clinical trials, there are also roadblocks. HIPAA compliance requirements were an early attempt to protect people from the negative impact of networked data. Reporting published by Cambridge University Press says: “Fundamental characteristics of big data challenge the structure of how we regulate human subjects research, the impact of HIPAA, and how we think of health care itself…HIPAA imposes a tangled web of regulation that hampers the use of healthcare data.”
Machine Learning And AI For Better Discovery Of Drugs
ML and AI may enable a quicker analysis of data sets gathered earlier and at a swifter rate for clinicians, ensuring higher reliability and efficiency in turn. The integration of synthetic control arms in mainstream research will offer new possibilities in terms of transforming the development of drugs.
With an increase in the count of data sources including health apps, personal wearables and other devices, electronic medical records, and other patient data, these may well become the safest and quickest mechanisms for tapping real-world data for better research into ailments with sizeable patient populations.
Researchers may achieve greater patient populations which are homogenous and get vital insights alongside. Here are some other points worth noting:
ML and AI tools may help unearth invaluable insights which will take a huge number of hours for human beings otherwise. They can generate results in only a few minutes.
Bigger pharmaceutical firms may have innumerable ongoing trials with several datasets. With multiple data points, there is a higher need for improved data analysis and management. Mismanagement of data may lead to costly errors otherwise.
Researchers can tap these techniques for swiftly identifying vital patterns and prospective trial-related complications on a real-time basis.
Data analysis is enabling the prediction of outcomes of clinical trials for new medicines. There are timelier and more accurate results/estimates and better risk and reward estimates for all stakeholders.
With better visibility into drug development risks, researchers are able to design clinical trials more effectively, expanding patient selection criteria and swiftly sifting through several parameters alongside.
Data analytics is enabling better decision-making throughout the drug development procedure, while also enhancing overall clinical trial efficiency with predictive modelling, identifying new potential candidate molecules for successful drug development with more certainty.
Through automation and big data, companies may provide real-time responses to clinical data insights, while developing more efficient trials and lowering trial time greatly.
Organizations are using Python and R for advanced data analysis and wider capabilities with regard to taking care of bigger data sets and streamlined procedures for proper result reproduction and lower chances of errors.
The outcomes of clinical trials are major metrics with regard to performance, at least as far as companies and investors are concerned. They are also the beginning of collaborations between patients, groups, and the healthcare sector at large. Hence, there is a clearly defined need for big data analysis in clinical trials as evident through the above-mentioned aspects.
Conclusion
Data analytics can be readily used for clinical trial design and analysis, expanding patient selection criteria, swiftly sifting through various parameters and helping researchers better target patients who match the criteria for exclusion and inclusion. Data analysis methods also enable better conclusions from data while also improving clinical trial design due to better visibility of the possible/predicted risk-reward outcomes.
References
1. Margolis R, Derr L, Dunn M, et al.The National Institutes of Health‘s Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data. J Am Med Inform Assoc 2014;21:957-8. [PMC free article] [PubMed] [Google Scholar]
2.https://www.indusnet.co.in/site/wp-content/uploads/2023/03/The-role-of-data-analytics-in-clinical-trial-design-and-analysis1239x-379.jpg
Student name :Vanam Naveen Kumar
Student ID 144/072023
Qualification: M. Pharmacy
e-mail id:naveenkumar.vanam2@gmail.com
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