data analysis

December 4, 2020: The Yale New Haven Health System as an Evidence Generation Ecosystem for Heart Failure (Tariq Ahmad, MD, MPH)

Posted on by Gina Uhlenbrauck

Speaker

Tariq Ahmad, MD, MPH
Director, Advanced Heart Failure Program
Yale School of Medicine and Yale New Haven Health

Topic

The Yale New Haven Health System as an Evidence Generation Ecosystem for Heart Failure

Keywords

Heart failure; Best practice alerts; Electronic health records; Risk prediction; Guideline-directed medical therapy; REVEAL-HF

Key Points

  • The REVEAL-HF study is a pragmatic randomized controlled trial testing an electronic alert system that informs clinicians about the 1-year predicted mortality for their patients with heart failure using validated data from the EHR.
  • It is important to use guideline-directed medical therapy for patients with heart failure. The hypothesis of the trial is that providing prognostic information for a patient with heart failure will lead to improved use of therapies and appropriate referral to subspecialties.

Discussion Themes

How did you get health system leadership and all of the clinicians, IT folks, and others to buy in to implementing your trial?

How can we make risk information valuable and actionable to healthcare providers?

Clinicians bring something to the table that an algorithm does not. It will be interesting to see how clinician behavior is affected by using the prediction models and interacting with the data.

Read more about the REVEAL-HF trial.

Tags

#pctGR, @Collaboratory1

August 31, 2020: Newly Validated Sample Size Formula Detects Heterogeneity of Treatment Effect in Cluster Randomized Trials

Posted on by Damon Seils

Cover of Statistics in MedicineIn a study supported by the NIH Collaboratory, researchers developed and validated a new sample size formula for detecting heterogeneity of treatment effect in cluster randomized trials. The work was published this month in Statistics in Medicine.

Cluster randomization is frequently used in pragmatic clinical trials embedded in healthcare systems. Although cluster randomized trials are typically designed to evaluate the overall treatment effect in a study population, investigators are increasingly interested in studying differential treatment effects among subgroups.

The NIH Collaboratory investigators used extensive computer simulations to validate the new formula. They illustrate the procedure in a dataset from a large clinical trial.

In a previous study published last year, the same research team used computer simulation models validated by real-data simulations to reveal the influence of baseline covariate imbalance on treatment effect bias.

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.

July 14, 2020: Grand Rounds Webinar Presents the New N3C Analytics Platform for COVID-19 Research

Posted on by Gina Uhlenbrauck

Watch the recent Grand Rounds webinar presented by Dr. Ken Gersing of the National Center for Advancing Translational Sciences and Dr. Robert Star of the National Institute of Diabetes and Digestive and Kidney Diseases to learn more about the COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C).

The N3C initiative aims to build a centralized national data resource that researchers can use to study COVID-19 and identify potential treatments as the pandemic continues to evolve. N3C is a partnership among the Clinical and Translational Science Awards Program hubs and the National Center for Data to Health, with overall stewardship by the National Center for Advancing Translational Sciences (NCATS).

The goals of N3C are to:

  • Rapidly collect and aggregate clinical, lab, and imaging data from hospitals, health plans, and CMS at the peak of the COVID-19 pandemic and as it evolves
  • Provide a longitudinal dataset to understand acute hospital and recovery phases
  • Understand pathophysiology of disease
  • Support clinical trials by identifying patients who might wish to participate in trials

Watch the Grand Rounds webinar or download the slides. For more details, visit the NCATS N3C website.

July 10, 2020: COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C) (Ken Gersing, MD; Robert Star, MD)

Posted on by Gina Uhlenbrauck

Speakers

Ken Gersing, MD
Director of Informatics, NCATS
National Institutes of Health  

Robert A. Star, MD
Director, Division of Kidney, Urologic, and Hematologic Disorders, NIDDK
Chief, Renal Diagnostics and Therapeutics Unit, NIDDK
National Institutes of Health  

Topic

COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C)

Keywords

COVID-19; Coronavirus; Pandemic; Data exchange; Data use agreement; Phenotypes; Data harmonization; Common data model; Fast Healthcare Interoperability Resources (FHIR); Synthetic data

Key Points

  • The National COVID Cohort Collaborative (N3C) initiative aims to build a centralized national data resource that the research community can use to study COVID-19 and identify potential treatments as the pandemic continues to evolve.

  • N3C is a partnership among the Clinical and Translational Science Awards Program hubs and the National Center for Data to Health, with overall stewardship by the National Center for Advancing Translational Sciences (NCATS).

  • The goals of N3C are to:
    • Rapidly collect and aggregate clinical, lab, and imaging data from hospitals, health plans, and CMS at the peak of the COVID-19 pandemic and as it evolves
    • Provide a longitudinal dataset to understand acute hospital and recovery phases
    • Understand pathophysiology of disease
    • Support clinical trials by identifying patients who might wish to participate in trials

Discussion Themes

The N3C analytics platform is cloud-based and provides a secure data enclave. Data can be received via multiple data models and transformed into a common analytic model for research.

As a centralized data model, N3C complements existing federated data models like PCORnet and OMOP. The tool does not replace the need for randomized controlled trials.

NCATS, FDA, and NCI are working together on common data model (CDM) harmonization so that data will be publicly available and reusable in human and machine-readable formats.

Read more on the NCATS N3C website as well as view a short video demonstration.

Tags

#pctGR, @Collaboratory1, @ncats_nih_gov

June 22, 2020: NIH Offers Methods Webinar on Stepped-Wedge Cluster Randomized Trials

Posted on by Damon Seils

The NIH Office of Disease Prevention will continue its Methods: Mind the Gap webinar series on July 14 with “Overview of Statistical Models for the Design and Analysis of Stepped Wedge Cluster Randomized Trials.” Dr. Fan Li of Yale University, a longtime participant in the NIH Collaboratory’s Biostatistics and Study Design Core Working Group, will lead the webinar.

The Methods: Mind the Gap series explores research design, measurement, intervention, data analysis, and other methods of interest in prevention science. The July 14 session will address the stepped-wedge cluster randomized design, which has received increasing attention in pragmatic clinical trials (PCTs) and implementation science research. Since the design’s introduction, a variety of mixed-effects model extensions have been proposed for the design and analysis of PCTs. Dr. Li will provide a general model representation and discuss model extensions as alternative ways to characterize secular trends, intervention effects, and sources of heterogeneity. He will also review key model ingredients and clarify their implications for the design and analysis of stepped-wedge cluster randomized trials.

Register in advance to join the online presentation. Registration is required.

May 28, 2020: New Updates to Design and Analysis Plan Chapters in the Living Textbook

Posted on by Damon Seils

The annual update of the Living Textbook has brought new content and organization to the Experimental Designs and Analysis Plan chapters. We invite you to explore these chapters and the external resources linked from the resources sidebar in each section.

The NIH Collaboratory Coordinating Center regularly refreshes content in the Living Textbook to improve the robust collection of resources it offers to the wider research community about how to plan and implement pragmatic clinical trials.

Sections of the Experimental Designs and Randomization Schemes chapter include:

  • Statistical Design Considerations
  • Cluster Randomized Trials
  • Randomization Methods
  • Choosing Between Cluster and Individual Randomization
  • Alternative Cluster Randomized Designs
  • Concealment and Blinding
  • Designing to Avoid Identification Bias
  • Additional Resources

Sections of the Analysis Plan chapter include:

  • Intraclass Correlation
  • Unequal Cluster Sizes
  • Accounting for Residual Confounding in the Analysis
  • Missing Data and Intention-to-Treat Analyses
  • EHR Data Extraction
  • Unanticipated Changes
  • Case Study: STOP CRC Trial

April 23, 2020: New Workshop Summary on the Design and Analysis of Pragmatic Clinical Trials

Posted on by Karen Staman

In 2019, NIH Health Care Systems Research Collaboratory held a comprehensive workshop to explore and discuss statistical issues encountered with embedded pragmatic clinical trials (ePCTs). The new Workshop Summary describes panel discussions with the principal investigators and statisticians of NIH Collaboratory Trials and the challenges and solutions encountered during the design and analysis of their trials.

The 4 panel discussions covered the following topics:

  • Measurement and Data: Outcomes, Exposures, and Subgroups Based on EHR Data
  • To Cluster or Not to Cluster?
  • Choosing a Parallel Group or Stepped-Wedge Design
  • Unique Complications

This Workshop Summary also provides lessons learned and recommends tools to help others design and analyze future ePCTs. For more on the design and analysis of pragmatic clinical trials, see the tools provided by the Biostatistics and Study Design Core and Living Textbook chapters on Experimental Designs and Randomization Schemes and Analysis Plans.

February 11, 2020: ADAPTABLE Roundtable Produces Consensus Statement on Analysis and Integration of Patient-Reported Data in Clinical Trials

Posted on by Damon Seils

A roundtable discussion organized by the NIH Collaboratory in 2017 has produced consensus findings on the analysis and integration of patient-reported health (PRH) data in clinical trials. The report is part of an effort by the ADAPTABLE Supplement project team “to address best practices for capturing PRH data in pragmatic studies and optimal analytic approaches for integrating PRH with other data sources.”

The consensus statement was published online ahead of print this month in the Journal of the American Medical Informatics Association.

The report discusses strengths and limitations of PRH data, approaches for ascertaining and classifying study end points, and methods for addressing incompleteness, data alignment, and data concordance. Roundtable participants used experiences from the ADAPTABLE trial as a case study to inform their discussions.

ADAPTABLE, the first major randomized comparative effectiveness trial conducted by the National Patient-Centered Clinical Research Network (PCORnet), seeks to determine the optimal dose of aspirin therapy for secondary prevention of atherosclerotic cardiovascular disease. The trial relies on both existing EHR data sources and PRH data.

This work was supported by a supplemental grant award to the NIH Collaboratory Coordinating Center from the National Center for Complementary and Integrative Health.

December 6, 2019: Millions More People, Stronger Collaborations: The New and Improved NIH Collaboratory Distributed Research Network (Richard Platt, MD, Kevin Haynes, PharmD, Denise Boudreau, PhD, Jerry Gurwitz, MD, Christopher Granger, MD)

Posted on by Gina Uhlenbrauck

Speakers

Richard Platt, MD, MS
Professor and Chair
Harvard Medical School
Department of Population Medicine

Kevin Haynes, PharmD, MSCE
Principal Scientist
HealthCore

Denise Boudreau, PhD
Senior Scientific Investigator
Kaiser Permanente Washington Health Research Institute

Jerry H. Gurwitz, MD
Professor of Medicine, Family Medicine and Community Health, and Population & Quantitative Health Sciences
University of Massachusetts Medical School
Executive Director, Meyers Primary Care Institute

Christopher B. Granger, MD
Professor of Medicine
Duke University

Topic

Millions More People, Stronger Collaborations: The New and Improved NIH Collaboratory Distributed Research Network

Keywords

Embedded clinical research; Distributed research network; Administrative claims data; Multisite research; Sentinel System; Electronic health data; National registries; Common data model; Curated research data

Key Points

  • The NIH Collaboratory Distributed Research Network (DRN) enables investigators funded by the NIH and other not-for-profit sponsors to collaborate with investigators based in health plans that participate in the FDA’s Sentinel System.
  • Examples from an array of real-world research studies highlight strengths of conducting collaborative research using the DRN.
  • Among the DRN’s attributes are the abilities to embed a randomized clinical trial in real-world clinical settings, to direct outreach to providers and patients/families, and to determine feasibility with high accuracy to allow confidence in planning of ambitious clinical trials.

Discussion Themes

The DRN is optimized for multicenter research and depends on partnerships. It was developed to enable productive research collaborations.

How do investigators who are not embedded in participating health systems learn to work effectively in the DRN?

Read more about the NIH Collaboratory’s DRN.

Tags
#pctGR, @Collaboratory1, @DeptPopMed, @HealthCoreRWE

November 8, 2019: Lumbar Imaging with Reporting of Epidemiology: Initial Results and Some Lessons Learned (Jeffrey Jarvik, MD, MPH, Patrick Heagerty, PhD)

Posted on by Gina Uhlenbrauck

Speakers

Jeffrey (Jerry) G. Jarvik MD MPH
Professor, Radiology, Neurological Surgery and Health Services
Adjunct Professor, Pharmacy and Orthopedics & Sports Medicine
University of Washington

Patrick Heagerty, PhD
Professor and Chair
Department of Biostatistics
University of Washington

Topic

Lumbar Imaging with Reporting of Epidemiology: Initial Results and Some Lessons Learned

Keywords

Embedded pragmatic clinical trials; Radiology imaging; LIRE; Stepped-wedge; Cluster randomization; Epidemiology; Back pain

Key Points

  • The LIRE NIH Collaboratory Trial evaluated whether prevalence benchmark data inserted into lumbar spine imaging reports would reduce overall spine-related healthcare utilization for patients referred from primary care.
  • The inserted intervention text urges caution when interpreting the presence of certain findings that are common in normal, pain-free volunteers.
  • While the study team found no decrease in spine-related healthcare utilization for the overall cohort, there was a small but potentially important effect on reducing opioid prescriptions.

Discussion Themes

A characteristic of stepped-wedge study design is that it yields two comparisons: between-group comparisons (clinic A vs clinic B) and within-group comparisons. But temporal trends can have an impact and must be adjusted for in the analysis.

For what type of intervention would a stepped-wedge design be suitable?

The hope is for a wider dissemination about interventions where radiologic testing is done and incidental findings are common.

Read more about the LIRE NIH Collaboratory Trial.

Tags
#pctGR, #PragmaticTrials, @Collaboratory1