Category Archives: Data analysis

Use of Data from Electronic Health Records to Customize Medical Treatments

Data analysis, Data quality, Electronic health records, ,

In a recent segment on NPR’s Morning Edition, commentators discuss the potential of using electronic health records to customize medical treatments.

Dr. Harlan Krumholz, a professor of medicine at Yale University, says comparing data in electronic health records with genomic information holds great promise for customizing individual treatments, but he warns that the quality of data collected in the medical record is not research quality. While researchers are making a positive start with initiatives such as the Precision Medicine Initiative (re-branded as the All of Us research program), medicine still has a long way to go to fully realize the potential of these data.

Dr. Harlan Krumholz will be presenting at an upcoming NIH Collaboratory Grand Rounds on January 13 from 1:00 – 2:00 p.m. ET. “What’s Next: People-Powered Knowledge Generation from Digital Health Data.” Join the meeting here.

The full article and audio can be found on NPR Shots, an online channel for health stories from the NPR Science Desk.

New Online Course from NIH: Pragmatic & Group-Randomized Trials

Biostatistics, Clinical trials, Cluster Randomized Trials, Data analysis, Pragmatic clinical trials, Stepped-wedge, , , , , , , ,

The National Institutes of Health’s Office of Disease Prevention (ODP) has just released a free, self-paced online course on designing and analyzing pragmatic and group-randomized trials. The course, which is presented by ODP Director Dr. David Murray, includes a series of seven video presentations plus slide sets, reference materials, and guided activities.

Course segments typically last 25 to 35 minutes. Presentations can be accessed individually and include the following topics:

Picture of course presenter Dr. David Murray
Course presenter Dr. David Murray, Director, Office of Disease Prevention, NIH (image courtesy NIH)

The course is designed for faculty, fellows, and graduate students who have had training in the basics of research design and regression analysis. Part 5 (“Examples”) of the presentation includes multiple examples drawn from NIH Collaboratory Demonstration Projects, including the Strategies and Opportunities to Stop Colorectal Cancer (STOP-CRC), Collaborative Care for Chronic Pain in Primary Care (PPACT), Trauma Survivors Outcomes and Support (TSOS), and Improving Chronic Disease Management with Pieces (ICD-PIECES) studies.

STOP CRC Trial: Analytic Challenges and Pragmatic Solutions

Cluster Randomized Trials, Comparative Effectiveness Research, Data analysis, Pragmatic clinical trials, , , , ,

Investigators from the STOP CRC pragmatic trial, an NIH Collaboratory Demonstration Project, have recently published an article in the journal eGEMs describing solutions to issues that arose in the trial’s implementation phase. STOP CRC tests a program to improve colorectal cancer screening rates in a collaborative network of Federally Qualified Health Centers by mailing fecal immunochemical testing (FIT) kits to screen-eligible patients at clinics in the intervention arm. Clinics in the control arm provided opportunistic colorectal-cancer screening to patients at clinic visits in Year 1 and implemented the intervention in Year 2. In this cluster-randomized trial, clinics are the unit of analysis, rather than individual patients, with the primary outcome being the proportion of screen-eligible patients at each clinic who complete a FIT.

The team dealt with various challenges that threatened the validity of their primary analysis, one of which related to potential contamination of the primary outcome due to the timing of the intervention rollout: for control participants, the Year 2 intervention actively overlapped with the Year 1 control measurements. The other challenge was due to a lack of synchronization between the measurement and accrual windows. To deal with these issues, the team had to slightly modify the study design in addition to developing a few sensitivity analyses to better estimate the true impact of the intervention.

“While the nature of the challenges we encountered are not unique to pragmatic trials, we believe they are likely to be more common in such trials due to both the types of designs commonly used in such studies and the challenges of implementing system-based interventions within freestanding health clinics.” (Vollmer et al. eGEMs 2015)

The Publish EDM Forum Community publishes eGEMs (generating evidence & methods to improve patient outcomes) and provides free and open access to this methods case study. Readers can access the article here.