THOUGHT LEADERSHIP

Having worked in the world of clinical research for 15 years, I can’t help but be baffled by the rampant misuse of the word “de-identified” as it relates to the classification of clinical data sets.  In the United States, “the HIPAA Privacy Rule provides federal protections for personal health information [PHI] held by covered entities and gives patients an array of rights with respect to that information.”  De-identified health information is not PHI and thus is not protected by HIPAA.

It’s a common misconception that if you remove patients’ names from a set of clinical data, then it becomes de-identified and is no longer governed by HIPAA.  However, to make a set of clinical data truly de-identified, you must remove much more than just the patients’ names. 

Home visits as part of clinical research have accelerated in recent years as we strive to find the right balance between facilitating study participation while accommodating participant’s busy everyday lives. Clinical trials in rare diseases are even more challenging than trials in other diseases due to a number of factors:

• Small number of eligible trial participants
• Complicated by heterogeneity among rare disease patients
• Most have no cure and manifest at a young age
• Less than 10% of rare diseases have a specific treatment
• Many have other debilitating conditions / physical limitations making it difficult to attend frequent study visits

The combination of home study visits and the right technology removes barriers to optimal patient recruitment, compliance and retention.

This is the last in a series of three posts on missing data imputation.  In the first post, we reviewed the underlying assumptions and limitations of single imputation methods, in particular, last observation carried forward (LOCF).  In the second post, we considered more sophisticated data imputation methods for longitudinal data, Mixed Models for Repeated Measures (MMRM) and Multiple Imputation (MI).  An important assumption for both MMRM and MI is that the data are ‘missing at random’ (MAR), if not ‘missing completely at random’ (MCAR).  But what if this assumption is not true for all the missing data?

Four former FDA commissioners and Dr. Peter Marks, the FDA official responsible for the oversight of vaccines, shared their thoughts on the FDA’s role in ensuring the safety and effectiveness of COVID-19 vaccines under extraordinary circumstances, in the ongoing pandemic.

The event, entitled “Safe and Effective COVID-19 Vaccination: The Path from Here” was hosted by the Duke-Margolis Center for Health Policy on September 10, 2020.  The speakers discussed the mountain of challenges across all aspects of developing and distributing a vaccine under expedited timelines: testing, reviewing, approving, monitoring safety events, and the logistics of distribution for vaccines that may require specialized storage conditions.

All clinical data managers (CDMs) are familiar with the difficulties of ensuring that laboratory values are compared to the local laboratory normal ranges that were valid at the time of the measurement. In studies with many local laboratories, and in global studies, the same test may be reported in different units across the study. To help alleviate these problems, some studies use central laboratories. Yet there are some problems with this approach as well. Shipping samples from study sites to the central laboratory incurs extra cost and runs the risk that the sample will be unusable when it arrives.