biostatistics

October 18, 2019: Playing with FHIR–Innovative Use Cases for the New REDCap EHR Integration Module (Paul Harris, PhD)

Posted on by Gina Uhlenbrauck

Speaker

Paul A. Harris, PhD
Director, Office of Research Informatics
Professor of Biomedical Informatics, Biostatistics, and Biomedical Engineering
Vanderbilt University Medical Center

Topic

Playing with FHIR–Innovative Use Cases for the New REDCap EHR Integration Module

Keywords

Fast Healthcare Interoperability Resources; FHIR; Data interoperability; Electronic health record; EHR; Electronic data capture; Clinical data; Research informatics

Key Points

  • REDCap (Research Electronic Data Capture) is a robust, web-based data exchange platform developed at Vanderbilt to assist the research community in implementation efforts.
  • The REDCap consortium has more than a million users. The platform is available at no cost to academic, nonprofit, and government organizations who join the consortium.
  • Innovative use cases are being conducted with REDCap and an Epic EHR system to increase data flow and remove dependency on a data warehouse.

Discussion Themes

How can we harmonize the REDCap approach with PCORnet’s common data model (CDM)?

FHIR is an HL7 standard for exchanging healthcare information electronically. Another use case integrates FHIR to democratize EHR extraction methods to improve efficiency in multisite clinical data collection.

How can researchers manage many-to-one mapping; for example, if the electronic case report form (CRF) has one field value but there are many values in the record?

Read more about the REDCap project.

Tags
#pctGR, @Collaboratory1

August 5, 2019: New Section of Living Textbook Addresses Missing Data in Intention-to-Treat Analyses

Posted on by Damon Seils

A new section of the NIH Collaboratory’s Living Textbook of Pragmatic Clinical Trials discusses challenges associated with missing data that result from noncompliance, crossover, and dropout.

Many randomized controlled trials use an intention-to-treat (ITT) analysis to measure the real-world effects of the intervention. The newly published section, Missing Data and Intention-to-Treat Analyses, considers the population-level causal effects in these trials when there is noncompliance or missing outcome data.

“One rationale for the ITT approach is that it evaluates the real-world effects of the intervention. However, a common misconception is that the ITT analysis will be unbiased regardless of crossover or missing data.”

The new section also introduces a white paper from the NIH Collaboratory’s Biostatistics and Study Design Core, “Analyses of Randomized Controlled Trials in the Presence of Noncompliance and Study Dropout.” This working document offers analysts a more detailed discussion of treatment effects in ITT analyses, including a case example and recommended strategies for estimating and reporting both ITT effects and average causal effects.

The Biostatistics and Study Design Core works with the NIH Collaboratory Trial teams to create guidance and technical documents regarding study design and biostatistical issues relevant to pragmatic clinical trials.

April 15, 2019: Registration Now Open for Workshop on the Design & Analysis of Embedded Pragmatic Clinical Trials (ePCT)

Posted on by Gina Uhlenbrauck

The NIH Health Care Systems Research Collaboratory is hosting a one-day workshop on the Design & Analysis of Embedded Pragmatic Clinical Trials (ePCTs) on May 2, 2019, in the Lister Hill Auditorium on the NIH Campus.

The workshop will include a series of moderated discussions that focus on issues of measuring trial outcomes from available data sources, potential randomization strategies, specific ePCT design considerations, and unique challenges associated with ePCTs. Panel discussions will utilize case examples from the Collaboratory repertoire and beyond to illustrate how clinical investigators and biostatisticians work to address research questions posed by specific trials.

The Workshop Website provides information on meeting logistics, agenda, and registration. There is also an option to attend the workshop remotely via the NIH Videoconference Center, and those details are also available at the Workshop Website.

February 21, 2019: Living Textbook Offers New Content on Design and Analysis of Pragmatic Clinical Trials

Posted on by Damon Seils

Members of the NIH Collaboratory’s Biostatistics and Study Design Core contributed 3 new sections to the Living Textbook exploring issues in the design and analysis of pragmatic clinical trials. The new sections offer insights into emerging issues in embedded pragmatic clinical trials and lessons learned from the NIH Collaboratory’s first round of NIH Collaboratory Trials.

  • The Designing to Avoid Identification Bias section addresses a type of selection bias that can occur in pragmatic clinical trials that use information from electronic health records to determine study population eligibility and in which the study intervention influences who undergoes screening or receives a diagnosis in clinical care.
  • The Alternative Cluster Randomized Designs section describes alternative design choices for cluster randomized trials and their implications for statistical power and sample size calculations. Modified cluster randomized designs, such as cluster randomization with crossover, may reduce the sample size required for a pragmatic clinical trial and may be particularly feasible in trials embedded in healthcare systems with electronic health records.
  • Case Study: STOP CRC Trial explores challenges in design and analysis that were faced in the Strategies and Opportunities to Stop Colorectal Cancer in Priority Populations (STOP CRC) trial, one of the NIH Collaboratory Trials. The case study illustrates how the study team dealt with pragmatic issues during the planning and conduct of the trial.

In addition to contributing content to the Living Textbook, the Biostatistics and Study Design Core works with the NIH Collaboratory Trials to address challenges in their statistical plans and study designs during the planning phase and to develop guidance and technical documents related to study design and biostatistical issues relevant to pragmatic clinical trials.

October 1, 2018: Dr. Greg Simon Uses a Pie Eating Contest Analogy to Explain the Intraclass Correlation Coefficient

Posted on by Karen Staman

In a new video, Dr. Greg Simon explains the intraclass correlation coefficient (ICC) with an analogy to a pie eating contest. The ICC is a descriptive statistic that measures the correlations among members of a group, and it is an important tool for cluster-randomized pragmatic trials because this calculation helps determine the sample size needed to detect an effect.

Greg Simon from NIH Collaboratory on Vimeo.

“When we randomize treatments by doctors, clinics, or even whole health systems, we need to think about how things cluster, and the intraclass correlation coefficient is the measure of that clustering. When we think about sample sizes in pragmatic clinical trials, it’s important to understand what an intraclass correlation coefficient actually is.”

For most pragmatic trials, the ICC will be between 0 and 1. If the outcomes in a group are completely correlated (ICC=1), then all participants within the group are likely to have the same outcome. When ICC=1, sampling one participant from the cluster is as informative as sampling the whole cluster, and many clusters will be needed to detect an effect. If there is no correlation among members of the groups (ICC=0), then the available sample size for the study is essentially the number of participants.

For more on the ICC, see the Intraclass Correlation section in the Living Textbook or this working document from the Collaboratory’s Biostatistics and Study Design Core.

July 30, 2018: Registration Open for 3rd Seattle Symposium on Health Care Data Analytics

Posted on by Damon Seils

Registration is open for the 3rd Seattle Symposium on Health Care Data Analytics. The symposium will bring together biostatisticians, health informaticists, epidemiologists, and other data scientists to discuss health research and methods that involve large health care databases.

Experts involved in national research initiatives that use large health care databases will discuss methodological challenges encountered in this setting and share ideas for addressing them. Speakers will share their research on:

  • statistical approaches to learning from electronic health care data;
  • methods for precision medicine; and
  • health policy.

Space is limited, and registration is required.

The event is sponsored by the Biostatistics Unit at Kaiser Permanente Washington Health Research Institute and the Department of Biostatistics at the University of Washington.

March 21, 2018: Dr. Rob Califf to Speak on Data Science at March 23 Grand Rounds

Posted on by Gina Uhlenbrauck

Robert Califf, MD, former FDA Commissioner and current Vice Chancellor for Health Data Science at Duke University School of Medicine, will present at NIH Collaboratory Grand Rounds on Friday, March 23 at 1 pm ET. The webinar will be broadcast live and is open to the public. Following the presentation, Dr. Califf will answer questions from the Grand Rounds audience.

As Director of Duke Forge, Duke’s interdisciplinary center for actionable health data science, Dr. Califf is currently working on initiatives designed to harness biostatics, machine learning, and sophisticated informatics approaches to improve health and healthcare. Dr. Califf is also an adjunct professor of medicine at Stanford University and is employed by Verily Life Sciences as a scientific advisor. Verily, part of the Alphabet (Google) family of companies, is aimed at transforming the growth of health-related data into practical applications.

Dr. Califf has been a pioneer in the fields of clinical, translational, and outcomes research, and the NIH Collaboratory looks forward to hearing his thoughts on the pragmatic applications of data that will advance health and health care strategies and practice.

Topic: Data Science in the Era of Data Ubiquity

Date: Friday, March 23, 2018, 1:00-2:00 p.m. ET

Meeting Info: To check whether you have the appropriate players installed for UCF (Universal Communications Format) rich media files, go to https://dukemed.webex.com/dukemed/systemdiagnosis.php.

To join the online meeting:
Go to https://dukemed.webex.com/dukemed/j.php?MTID=m1a4a0665a615ae0382440edecedbdd33

December 7, 2017: Dr. Greg Simon Explains Individual, Cluster, and Stepped-Wedge Randomization in a New Prop Video

Posted on by Karen Staman

In a new video in the Living Textbook, Dr. Greg Simon describes the differences between individual, cluster, and stepped-wedge randomization using props, including marbles, Play-Doh, and glassware.

“In the end, it’s all about randomly assigning who gets which treatment, or who gets which treatment when, so that we’re able to make some un-biased judgement about which treatment is really better.” —Greg Simon, MD

October 18, 2017: NIH Collaboratory Core Working Group Interviews: Reflections from the Biostatistics and Study Design Core

Posted on by Gina Uhlenbrauck

We recently asked Dr. Liz DeLong, Chair of the Biostatistics and Study Design Core, to reflect on the first 5 years of the Core’s work and the challenges ahead. She says the biggest impact of the Core has been working with the individual NIH Collaboratory Trials to provide a sounding board to discuss statistical challenges. Further, Core members have contributed to new knowledge through manuscripts that address key methodological issues related to pragmatic clinical trials. She’s hoping the Core will continue to push the boundaries of statistical methods in the coming years.

“The statisticians on the individual trials have not only developed excellent statistical methods for their own studies, but also contributed substantively to the Core.” Dr. Liz DeLong

Download the interview (PDF).