Insights & Innovators: Generating Rapid Response to Health Authority Inquiry Using Real-World Data and Advanced Analytics

Q&A with Jin Xia, PhD

Dr. Xia recently spoke about the ways in which real-world data and advanced analytics using Panalgo’s IHD Analytics platform can be leveraged to quickly and efficiently to respond to an inquiry from a health authority.

Q: Dr. Xia, can you provide some background on what prompted the need for a rapid study?

A: Takeda received an inquiry from a health authority following the confirmation of a safety signal for an adverse event for one of our products. I’ll refer to the event as Event X and the product as Drug Y for confidentiality reasons. The health authority asked us to include Event X in the Warnings and Precautions section of the package insert.

The event was rare, but treatable. We agreed that the event needed to be included in the Adverse Drug Reactions (ADR) section of the package insert, but not in the Warnings and Precautions section of the label. We had just 11 days to evaluate the association based on additional relevant data and provide evidence to justify our stance with the health authority.

Q: Why was it important to include the event in the Adverse Drug Reaction section and not the Warnings and Precautions section of the package insert?

A: The Warnings and Precautions section is intended to provide information to the patient and provider regarding something that is clinically significant or life-threatening. The purpose is to highlight events that should be actively monitored for safety purposes. Inclusion of an event in this section potentially raises greater concern regarding the ADR’s impact on the drug’s risk-benefit profile that we felt was unwarranted. Ultimately, we want to be transparent about known ADRs associated with use of the drug which is why it was recommended to add this event to the ADR section of the package insert, while providing complete and accurate safety information in a way that appropriately guides the discussion between the patient and their provider regarding the benefits and risks of the drug. . For that reason, we wanted to further evaluate the event and prove to the health authority that it did not meet the Warnings and Precautions criteria.

Q: How did you plan to meet this tight timeline?

A: We knew that population-based epidemiological data were needed to further evaluate the strength of the association between the exposure and the event. To address the request in such a short time we decided to use Panalgo’s IHD analytics platform to perform a population-based retroactive analysis based on real-world data.

Q: How did you design your analysis?

A: First we wanted to determine the number of patients who used Drug Y during the study period, so we used a cross-sectional analysis designed to describe patient characteristics and estimate the prevalence of Event X. We then calculated the continuous Drug Y exposure for these patients. In addition, we evaluated the characteristics of patients with and without Event X across multiple demographics (sex, race, age, etc.) We also used the characteristics analysis to obtain incidence and prevalence rates for Event X. Finally, we conducted a time-to-event analysis to assess the relative risk of Event X among Drug Y users compared with nonusers.

Q: What were your primary findings?

A: Out of about 1.5 million Drug Y users, less than 0.1% had an occurrence of Event X, demonstrating that the adverse event was rare. We also found that the average age of patients at their first diagnosis of Event X was older when exposed to Drug Y and that patients with Event X had used 3x more day’s supply of Drug Y compared to those without Event X. We also found that use of Drug Y was not associated with the risk of Event X. Based on these findings, we were able to generate real-world evidence to successfully make our case to the health authority in a timely manner.

Q: How quickly were you able to conduct the analysis using IHD?

A: About seven days in total. It took us two days from receipt of the initial health authority request to validate the data and develop an approach using IHD. The analysis took only five days from the start of the analysis to completion and generation of the report in IHD. We delivered our findings to the health authority four days prior to the initial due date.

Q: What decision did the health authority ultimately make?

A: They agreed with Takeda to add Event X to the ADR section of the country specific package insert. They also agreed that Event X does not meet the criteria to warrant upgrading to the Warnings and Precautions section of the package insert.

The Takeda Global Patient Safety Team believes that the real-world evidence generated from the IHD analysis was critical in informing the health authority decision to not upgrade the adverse event to the Warnings and Precautions section of the package insert. From a clinical perspective, the ability to utilize IHD to generate risk estimates for the event in question allowed for the adverse event to be appropriately reported in the package insert as needed to protect patient safety without potentially having a negative impact on prescribing decisions.

Q: Why did you choose IHD for this important analysis?

A: First, IHD is a validated tool that provides reliable results from healthcare data – which was very important to us. With IHD, our team can easily access healthcare data to address questions such as this health authority request quickly. By offering a wide range of healthcare data sources, IHD allows us to capture the key data elements of interest such as the extremely rare Event X in this project.

I think IHD users would agree that implementing advanced statistical analyses in the platform is extremely easy and does not require the need for complex programming. It is user friendly, and we have seen that it really improves the workflow efficiency. In addition, we were able to generate the report quickly with automated export of results and detailed analysis and design information from IHD. This information and results were already included in the output.

Finally, the IHD support team is always responsive and provided us with solutions for every question I asked which is amazing. Overall, it was a great experience for me working with IHD to provide the quick response we needed in this case.


Jin Xia is the Senior Manager, Safety Pharmacoepidemiology at Takeda with expertise in developing pharmacoepidemiologic strategies to support benefit-risk profile of medicinal products in neuroscience and immunology. Prior to joining Takeda, Jin obtained her PhD in Philosophy, Epidemiology with a Minor in Health Policy and Management from the Indiana University Richard M. Fairbanks School of Public Health.