data analysis

October 30, 2019: Baseline Covariate Imbalance Influences Treatment Effect Bias in Cluster Randomized Trials

Posted on by Damon Seils

In a study supported by the NIH Collaboratory, researchers found that imbalance in individual-level baseline covariates influences bias in the observed treatment effect in cluster randomized trials. Using race as an example, the study highlights the importance of reducing covariate imbalance in the design stage of cluster randomized trials and of using statistical analysis techniques to minimize the resulting bias.

The innovative study, published in Contemporary Clinical Trials, used computer simulation models validated by real-data simulations from a large clinical trial to examine the influence of baseline covariate imbalance on treatment effect bias. They found that bias was proportional to the degree of baseline covariate imbalance and the covariate effect size. In the simulations, trials with larger numbers of clusters had less covariate imbalance. Statistical models that adjusted for important baseline confounders were more effective than unadjusted models in minimizing bias.

The authors recommend several design approaches and statistical analysis techniques for both reducing covariate imbalance and minimizing bias. Using the results of available prior data can help researchers identify important baseline confounders when designing cluster randomized trials.

This work was supported within the NIH Collaboratory by the NIH Common Fund through a cooperative agreement from the Office of Strategic Coordination within the Office of the NIH Director, and by a research supplement from the NIH Common Fund to promote diversity in health-related research.

September 27, 2019: Preparing for Clinical Trial Data Sharing and Re-use: The New Reality for Researchers (Rebecca Li, PhD, Frank Rockhold, PhD)

Posted on by Gina Uhlenbrauck

Speakers

Rebecca Li, PhD
Executive Director, Vivli
Co-Director of Research Ethics, Harvard Center for Bioethics
Harvard Medical School

Frank W. Rockhold, PhD
Professor of Biostatistics and Bioinformatics
Duke Clinical Research Institute
Duke University Medical Center

Topic

Preparing for Clinical Trial Data Sharing and Re-use: The New Reality for Researchers

Keywords

Data sharing; Individual patient data; Open access; Raw data; ICMJE; Research dissemination

Key Points

  • Open access to individual patient data from clinical trials is a critical tool for research in health care. Despite the challenges, the question is not whether data should be shared, but rather how and when access should be granted.
  • Preparing data for reuse is often an afterthought—yet it is a new reality for researchers and institutions.
  • As of January 1, 2019, the International Committee of Medical Journal Editors (ICMJE) requires registration of a trial’s data sharing plan at the time of trial registration.
  • Institutions or teams should begin their data sharing program planning at least 18 months before a major publication (or regulatory approval).

Discussion Themes

FAIR data are data that meet standards of findability, accessibility, interoperability, and reusability.

How do we manage scientific integrity, replication, and validity given that data sharing opens a study to multiple people asking the same or related questions in potentially different ways using different methods?

How do we plan for a future that rewards data quality and reuse?

Read more about data sharing from ICMJE, NIH Office of Science Policy, and the National Academy of Medicine.

Tags

#pctGR, @Collaboratory1, @VivliCenter, @FrankRockhold

August 16, 2019: Introducing the Digital Medicine Society (Andy Coravos, MBA, Jen Goldsack, MS, MBA)

Posted on by Gina Uhlenbrauck

Speakers

Andy Coravos
CEO, Elektra Labs
Fellow, Harvard-MIT Center for Regulatory Science
Co-founder, Digital Medicine Society (DiMe)

Jen Goldsack, MS, MBA
Interim Executive Director, DiMe
Portfolio, Strategy & Ops, HealthMode

Topic

Introducing the Digital Medicine Society

Keywords

Digital medicine; Mobile health; Digital technologies; Wearable health devices; Connected devices; Cybersecurity

Key Points

  • Digital medicine is a rapidly evolving field that is by nature multidisciplinary and introduces new considerations for the healthcare community.
  • The Digital Medicine Society (DiMe) sits at the intersection of two communities: healthcare and technology. The Society is helping to move the field of digital medicine forward by developing a common language for diverse stakeholders from engineers and ethicists to payers and providers.
  • The U.S. healthcare system has strong protections for patients’ biospecimens like blood or genomic data, but what about digital specimens?

Discussion Themes

Are digital medical technologies worthy of the trust we place in them?

Should there be a Hippocratic Oath for manufacturers, organizations, and individuals delivering care through connected medical devices?

Read more about the emerging field of digital medicine and learn more about the Digital Medicine Society (DiMe), the professional home for those who practice and develop products in the digital era of medicine.

Tags

#DigitalMedicine, #pctGR, @Collaboratory1, @_DiMeSociety

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.

June 7, 2019: Meeting Materials from the 2019 NIH Collaboratory Steering Committee Meeting

Posted on by Gina Uhlenbrauck

The Collaboratory has made available all the presentations from their recent Steering Committee meeting held in Bethesda May 1-2, 2019.

Highlights of Day 1 included updates on the progress and sustainability of the NIH Collaboratory, perspectives on the landscape of embedded PCTs (ePCTs) and the need for real-world evidence, challenges and lessons learned from the UH3 NIH Collaboratory Trials, updates on progress and transition plans from the UG3 NIH Collaboratory Trials, and discussions on data sharing policy and planning. Day 2 featured an intensive workshop hosted by the NIH with the goal of starting discussions on statistical issues with ePCTs.

View or download the meeting materials on the website.

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.

January 22, 2019: New Self-Paced ePCT Training Course Available

Posted on by Gina Uhlenbrauck

The NIH Collaboratory is pleased to announce the availability of a new self-paced, 10-module introductory course on how to design, conduct, and disseminate embedded PCTs (ePCTs). This course presents condensed material from the inaugural ePCT Training Workshop held in 2018 and provides users with important things to know and do when designing an ePCT, along with helpful links to additional learning resources within the Living Textbook.

Also available in the Living Textbook are links to videocast workshops hosted by the NIH on a range of ePCT topics including:

  • Embedded PCTs of therapeutic A versus B interventions
  • Unique opportunities for disseminating, implementing, and sustaining evidence-based practices into clinical care
  • Ethical and regulatory issues of PCTs

For these and other ePCT resources, visit the Training Resources webpage.

October 12, 2018: MDEpiNet RAPID and SPEED Projects: Leveraging Real World Evidence to Get Better, Faster, Cheaper Medical Devices for Physicians and Patients (Renee Mitchell, MT, CLS, Terrie Reed, MSIE, Roseann White, MA)

Posted on by Gina Uhlenbrauck

Speakers

Renee Mitchell, MT(ASCP), CLS(NCA)
Regulatory Affairs
Boston Scientific Corporation, Inc.

Terrie Reed, MSIE
Senior Advisor for UDI Adoption
U.S. Food and Drug Administration (FDA)

Roseann White, MA
Director of Innovative Clinical Trial Statistics
Duke Clinical Research Institute

Topic

MDEpiNet RAPID and SPEED Projects: Leveraging Real World Evidence to Get Better, Faster, Cheaper Medical Devices for Physicians and Patients

Keywords

Medical devices; Real-world evidence; Medical Device Epidemiology Network; MDEpiNet; Unique device identifier; UDI

Key Points

  • In partnership, clinicians, device developers, and FDA can benefit from the use of real-world evidence on medical devices:
    •  Clinicians can contribute to the generation of real-world evidence.
    •  Device manufacturers can use real-world evidence to evaluate and release new devices and expand indications.
    •  Regulatory bodies can increase the use of patient-level data for device approval.
  • Unique device identifiers (UDIs) make it possible to follow medical devices longitudinally, advancing the quality of real-world evidence and allowing more sophisticated analyses.

Discussion Themes

The vision for the future is that registries will transform into big data solutions using multiple sources and will be more robustly integrated with electronic health records (EHRs). Both EHRs and registries will play a role.

More organizations as partners brings greater diversity, advancing better data and results. When stakeholders work together, learning curves can be accelerated toward a transformational approach to real-world evidence.

Tags

#MedicalDevices, #pctGR, @PCTGrandRounds, @Collaboratory1, @MDEpiNet_ppp

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.