Pharmaceutical Clinical Trials and Data Analytics

Pharmaceutical companies and other organizations that rely on clinical trials are increasingly pushing for greater informational transparency and sharing of patient data.

The rise of sophisticated analytics has allowed for more insights than ever to be extracted from clinical data. In addition to aiding research and development, clinical data can be used to benefit patients, years or even decades after they have participated in a trial.

Unfortunately, pharmaceutical companies and research institutions do not have unfettered access to clinical trial data, especially down to the level of the individual patient. Trial participants understandably want to retain their privacy and many regulations prohibit the improper disclosure or use of personal information, such as patient ages or specific medical conditions.

Where Access to Clinical Trial Data Provides Value

Patient-Level Data

Datasets from most clinical trials contain detailed information on individual participants. Access to patient-level data can not only allow for more granular analyses, but patient-level data is also valuable when it comes to quality checks. If unexpected side effects suddenly become associated with a certain medication, going back through the trial data and performing analytics at the patient level can reveal insights into the development.

Access to patient-level trial data also helps to optimize the value of that information through secondary analysis. For example, both Pfizer and Moderna have massive amounts of clinical trial data related to the development of their individual COVID-19 vaccines. A secondary analysis involving trial data from both companies could theoretically provide many new insights about the novel coronavirus.

International Data Collaboration

Access to more data can also facilitate pharmaceutical research on an international scale. The United States, Europe, China, India, and other jurisdictions all have agencies that oversee the approval of new medications based on established guidelines. For a new drug to get approval by the FDA, for instance, the clinical trials for the drug must be run at U.S. institutions. If regulations can be effectively navigated, such as through clinical data anonymization techniques, it opens up broader possibilities for both research and drug approval.

A New Perspective on Existing Data

Access can also facilitate subsequent analyses of a clinical dataset with objectives that differ from that of the original analysis. Follow-up analyses can help researchers gain a deeper understanding of the original trial and possibly unlock additional insights.

Integrating Trial Data with Real-World Data

Access to clinical trial data can also facilitate more representative datasets. Clinical trial populations tend to be very unique cohorts that do not always reflect the broader population. With access, clinical trial data can be compared with subsequent real-world data.

Data Anonymization in Clinical Trials and Analytics

Access to clinical trial data must also be balanced with protections for the privacy of individual participants.

This can be done with data anonymization techniques that obfuscate or eliminate identifying aspects of patient records. Data anonymization in clinical trials should be calibrated such that the utility of data is maintained. Additionally, data anonymization techniques should maintain the integrity of the original dataset. If the integrity of pharmaceutical trial data is corrupted by poorly calibrated anonymization, it could pose a significant public health threat.

In the U.S., the Health Insurance Portability and Accountability Act (HIPAA) outlines two approaches for anonymization. The Expert Determination approach involves a clinical trials data anonymization analyst applying statistical techniques to make the possibility of identifying individuals incredibly difficult or impossible. 

The Safe Harbor approach involves the removal of 18 specific types of identifying information from individual records, including name, Social Security number, telephone number, IP addresses, and license plate numbers. Many of these identifiers are not typically collected in the course of a pharmaceutical trial.

Accessing More Clinical Trial Data with Blind Query from TripleBlind

To address the challenges associated with sharing clinical data, TripleBlind has developed a unique set of data tools called Blind Query.

This innovative suite of data tools allows users to perform remote data queries while keeping privacy intact. Users can search datasets, join data sets, perform analyses, and create reports — all without needing to obtain direct access to sensitive data. With Blind Query tools, data operations are always performed remotely and can even be in multiple geographical or organizational silos.

The Blind Query suite of data tools can perform three main functions:

  • Blind join. Users can apply SQL-like methods to private tabular datasets to identify specific values, then extract those values to join with their own dataset. Data providers control access to specific data columns, and non-matched data is never revealed by Blind Join operations. Blind Join can perform operations on millions of records and identify non-exact (fuzzy) matches.
  • Blind string search. Users can conduct standard searches of text data without gaining access to non-matched text. Data providers are protected, and users can extract only the essential information they need.
  • Blind stats. Users can generate a report of descriptive statistics on private datasets, which is an essential function for understanding the demographics of clinical trial populations. Blind Stats also enables multi-party data collaborations by allowing participants to understand the qualities of a dataset, without compromising privacy.

TripleBlind offers a wide range of privacy-preserving data tools, including the Blind Query suite. If you would like to learn more about how TripleBlind can facilitate data access and collaborations, please contact us today.