Data analytics plays a key role in modeling clinical trials. Clinical researchers have been embracing machine learning algorithms throughout various stages in the drug discovery process. ML algorithms are applied to clinical trial data to identify predictive patterns and correlations between clinical outcomes, patient demographics, drug response phenotypes, medical history, and genetic information. Predictive analytics can improve clinical research by accelerating trials by modeling clinical outcome probability, resulting in better treatment decisions and reduced clinical trial costs. Data has become a critical component of the clinical sector since the emergence of the human genome.

The Role of Clinical Trials

Pharmaceutical companies use clinical trials to learn more about how drugs function and the potential side effects they carry before human testing. In clinical trials, new medicines are tested on human subjects to determine their safety and effectiveness. They are often conducted in three phases. The first phase of a trial begins after the completion of the preliminary lab and animal testing, and the approval of the study design by a research ethics committee. Clinical trials are conducted in the second phase on a small number of humans to test the effectiveness and patient tolerance of the medicine. Trials conducted in the third phase aim to confirm the medicine’s effectiveness, monitor side effects, and collect information that helps determine if the FDA should approve the medicine for retail.

Data analytics allows organizations to analyze all forms of data to identify patterns and insights that aid business intelligence and decision-making. The best data analytics solutions support the end-to-end analytical process of access, preparation, analysis, operationalizing analytics, and monitoring results. Leveraging insights is key to innovation and gaining a competitive advantage. Several big data analytics examples include anomalies detection, customer data management, risk management, fraud detection, personalization and customization, market research, and analyzing operations.

Data in Clinical Trials

Clinical trials increasingly use real-world data like electronic health records (EHRs), insurance claims, and billing data in drug development. Combining different eligibility criteria with real-world data provides deeper insights on the impact of trail design on the patient population and better predicts generalizability once the medication is approved. The application of predictive analytics to clinical research helps improve the overall success rate of clinical studies.

It takes drugmakers over ten years and billions of dollars to develop a drug and have it approved for retail. It’s not uncommon for drugs to be deemed harmful in clinical studies due to side effects, adverse drug interactions, or the causing of medical conditions. Sometimes medications on the market are considered to be unsafe for use years after being approved, such as the popular heartburn drug Zantac. Patients who took prescription Zantac and developed various types of cancer are entitled to financial compensation for their injuries via a class-action lawsuit represented by TorHoermanLaw. The Zantac lawsuit is a legal claim against the manufacturers of Ranitidine and Zantac, both of which are linked to cancer.

Use Cases of Analytics in Clinical Studies

There are several use cases of analytics in clinical studies. Researchers can predict clinical trial outcomes based on genetics, age, medical history, and other data. Predictive analytics can detect setbacks during the trial by analyzing real-world evidence. Analytics can predict side effects to medications and which patients are most likely to experience them. Machine learning modeling can make multi-drug interaction predictions and identify lower-risk interactions. It can also use clinical data to predict clinical trial enrollment. Analytics can also indicate clinical study completion, including dropout rates.

Utilizing the latest methodologies to assess clinical trials is the best way to achieve more accurate and effective results to improve patient care.