data analysis

January 3, 2024: Special Biostatistics Series Concludes With Missing Data in Cluster Randomized Trials

Posted on by Damon Seils

In this Friday's PCT Grand Rounds, Rui Wang of Harvard Medical School will offer the final session in our special series, Advances in the Design and Analysis of Pragmatic Clinical Trials, with "Methods for Handling Missing Data in Cluster Randomized Trials." The session will be held on Friday, January 5, at 1:00 pm eastern.

Wang is an associate professor of population medicine and the director of the Division of Biostatistics in the Department of Population Medicine at Harvard Medical School and the Harvard Pilgrim Health Care Institute. She is also an associate professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a longtime member of the NIH Pragmatic Trials Collaboratory's Biostatistics and Study Design Core Working Group.

This session's moderator, Fan Li, is an assistant professor of biostatistics at the Yale School of Public Health.

Join the online meeting.

This special Grand Rounds series includes moderated webinar discussions that bring together biostatisticians, clinical trials methodologists, and investigators to discuss challenges and share lessons learned in the design, implementation, and analysis of pragmatic trials. Download the series flyer and see the full schedule below, including archived webinar recordings and slides from previous sessions.

All sessions are free and open to the public. No registration is required.

November 29, 2023: Special Biostatistics Series Continues With Guidelines for Stepped-Wedge Trials

Posted on by Damon Seils

In this Friday’s PCT Grand Rounds, Jim Hughes of the University of Washington will continue our special series, Advances in the Design and Analysis of Pragmatic Clinical Trials, with his presentation, “Guidelines for Design and Analysis of Stepped-Wedge Trials.” The session will be held on Friday, December 1, at 1:00 pm eastern.

Hughes is a professor emeritus of biostatistics at the University of Washington. This session’s moderator, Patrick Heagerty, is a professor of biostatistics at the University of Washington and a cochair of the NIH Pragmatic Trials Collaboratory’s Biostatistics and Study Design Core.

Join the online meeting.

This special Grand Rounds series will include additional moderated webinar discussions that bring together biostatisticians, clinical trials methodologists, and investigators to discuss challenges and share lessons learned in the design, implementation, and analysis of pragmatic trials. Download the series flyer and see the full schedule below.

All sessions are free and open to the public; no registration is required.

March 8, 2023: Biostatistics Core Sponsors This Week’s PCT Grand Rounds on Estimands in Cluster Randomized Trials

Posted on by Damon Seils

Headshot of Brennan KahanIn this Friday’s PCT Grand Rounds, Brennan Kahan of University College London will present “Estimands in Cluster-Randomized Trials: Choosing Analyses That Answer the Right Question.” This session is sponsored by the NIH Pragmatic Trials Collaboratory’s Biostatistics and Study Design Core Working Group.

The Grand Rounds session will be held on Friday, March 10, 2023, at 1:00 pm eastern.

Kahan is a senior research fellow in the Institute of Clinical Trials and Methodology at University College London.

Join the online meeting.

March 30, 2022: Two Weights Make a Wrong: New Article From the Biostatistics and Study Design Core

Posted on by Karen Staman

Contemporary Clinical TrialslsIn a new article from the NIH Pragmatic Trials Collaboratory Biostatistics and Study Design Core, the authors share analytic considerations for cluster randomized trials with hierarchical nesting of participants within clusters. The authors illustrate the problem using theoretical derivations, a simulation study, and data from the STOP CRC NIH Collaboratory Trial as an example.

“We conclude that an analysis using both an exchangeable working correlation matrix and weighting by inverse cluster size, which may be considered the natural analytic approach, can lead to incorrect results. That is, two weights make a wrong. The bias is minimal when there is homogeneity of treatment effects according to cluster size but unacceptable when there is heterogeneity of treatment effects according to cluster size. In addition, we show that only an analysis with an independence working correlation matrix and weighting by inverse cluster size always provides valid results for the UATE [unit average treatment effect] estimand.”

Read the full article.

December 16, 2021: NIH Collaboratory Publishes COVID-19 Checklist for Statistical Analysis Plans in Pragmatic Trials

Posted on by Damon Seils

Thumbnail image of the COVID-19 checklistA new tool from the NIH Collaboratory assists investigators in identifying impacts of the COVID-19 public health emergency on ongoing pragmatic clinical trials. The Statistical Analysis Plan Checklist for Addressing COVID-19 Impacts summarizes impacts on trial conduct that study teams should document, measure, analyze, and report.

The new checklist was developed by the NIH Collaboratory’s Biostatistics and Study Design Core Working Group. Since the beginning of the COVID-19 pandemic, many of the NIH Collaboratory Trials have had to postpone recruitment, alter methods of participant engagement, and modify tools for research assessment and intervention delivery.

The leaders of the Biostatistics Core, Dr. Patrick Heagerty and Dr. Liz Turner, spoke in a recent interview about the impacts of the pandemic on the NIH Collaboratory Trials. Early next year, the Coordinating Center will report the results of a survey of the study teams about their experiences with these impacts.

Download the Statistical Analysis Plan Checklist for Addressing COVID-19 Impacts.

April 16, 2021: Minnesota EHR Consortium COVID-19 Project: A Statewide Collaboration to Inform Vaccine Equity (Paul E. Drawz, MD, MHS, MS; Tyler Winkelman, MD, MSc)

Posted on by Gina Uhlenbrauck

Speakers

Paul E. Drawz, MD, MHS, MS
Associate Professor
Division of Renal Disease and Hypertension
University of Minnesota

Tyler N.A. Winkelman, MD, MSc
Co-Director, Health, Homelessness, and Criminal Justice Lab
Associate Director, Virtual Data Warehouse
Hennepin Healthcare Research Institute

Topic

Minnesota EHR Consortium COVID-19 Project: A Statewide Collaboration to Inform Vaccine Equity

Keywords

COVID-19; Electronic health records (EHRs); Data analysis; Research consortium; Healthcare systems; Population health; Distributed data network; Vaccine equity

Key Points

  • The EHR Consortium’s COVID-19 vaccine project aims to inform policy and practice through data-driven collaboration among members of Minnesota’s health care community.
  • The collaborative network can monitor population-level health metrics and analyze changes over time using aggregations of data to inform public health policy. Sources of data include EHRs, census data, state-wide electronic immunization records, and population data.
  • The COVID-19 vaccine dashboard is updated weekly and provides data at the ZIP level by age categories and race/ethnicity.
  • Minnesotans who have received a COVID-19 vaccine (any source) and had a visit at a consortium site in the last 10 years (~90 percent of the state population) are reflected in the dashboard.

Discussion Themes

How were you able to convene this consortium during a pandemic year?

Was your hashing algorithm home-grown or did you have an outside partner?

In the future, this infrastructure will be expanded to incorporate smaller health systems and additional content expertise around comorbidities, disease prevalence, and identification of disparities in near real-time.

Read more about the MN EHR Consortium at Hennepin Healthcare and the University of Minnesota Clinical & Translational Science Institute.

Tags

#pctGR, @Collaboratory1

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