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The Hidden Problem in Dermatology Research

David M. Miller

2026-07-24

SoCO Research Forum · Metascience

The Hidden Problem in Dermatology Research

And what we can do about it: analytic search space, statistical inference, and a systems-level effort to make the analytic context of evidence visible.

Analytic Search Space Statistical Inference Reproducibility Open Science Open Inference Project

Program

SoCO Research Forum

Presenter

David M. Miller

Date

July 24, 2026

Meeting recap

People represented 38 Unique people after reconnect and duplicate-name reconciliation
Median time together 92 min Half the room stayed at least this long
Stayed ≥ 60 minutes 79% A sustained Research Forum conversation

The July Research Forum was intentionally exploratory. The point was not to present a finished project, but to expose the Open Inference Project early enough that colleagues could challenge the premise, the measurement strategy, and the path from a dermatology corpus to a broader research program.

🏅 Discussion standouts

A special thank-you to colleagues whose questions, critiques, and perspectives helped shape the conversation around analytic search space and the Open Inference Project.

David M. Miller Lisa Zaba Vishal Patel Isaac Brownell

Open Inference Project logo

The Open Inference Project

Making the analytic context of evidence visible

The conjecture

Scientific results do not arrive without context. Their meaning depends on the questions asked, the populations and outcomes examined, the models that were fit, and the alternative analytic paths that were available.

This Research Forum presentation began with a simple conjecture: the analytic context of evidence is systematically underappreciated. Published results are highly visible, while much of the analytic process that produced them can be difficult for readers to reconstruct.

Central Idea

The problem is not analytic complexity. The problem is analytic complexity that readers cannot see.

Where analytic search space comes from

Multiple outcomes

Primary, secondary, exploratory, and time-dependent outcomes create multiple reasonable questions within the same study.

Multiple models

Covariate sets, functional forms, model specifications, and variable definitions can each change the analysis.

Multiple subgroups

Age, disease severity, biomarkers, and other patient characteristics create additional analytic pathways.

Multiple decisions

Inclusion criteria, missing-data handling, transformations, and other defensible choices expand the space of possible analyses.

Studying the analytic structure of a literature

Rather than treating analytic flexibility and multiplicity as purely theoretical concerns, the project asked an empirical question: how large is the analytic search space in contemporary dermatology research?

An initial manual review suggested a substantial analytic search space. The work was then expanded into a structured, reproducible pipeline designed to characterize statistical reporting patterns across a much larger corpus of articles from the Journal of the American Academy of Dermatology.

Early Signal

Across the JAAD corpus, statistical testing is highly visible, while multiplicity adjustment, preregistration, and alternative inferential frameworks appear far less often.

What the review suggests

  • Researchers often have many reasonable ways to analyze the same data.
  • P-values are extremely common in the published dermatology literature.
  • Adjustments for multiple hypothesis testing are comparatively uncommon.
  • Preregistration is rare outside clinical trials.
  • Bayesian approaches are rarely used.
  • Analytic flexibility frequently remains implicit rather than directly visible to the reader.

What can we do about it?

Investigator level

Improve how individual studies are designed, analyzed, interpreted, and shared. Define questions clearly, distinguish confirmation from exploration, prespecify consequential choices when feasible, and communicate uncertainty.

Systems level

Move beyond transparency within one paper and make the analytic structure of an entire evidence base easier to discover, examine, compare, and interpret.

A Two-Level Response

Transparent individual studies are necessary, but they do not by themselves make the structure of a literature visible.

The Open Inference Project

The Open Inference Project is a research program focused on the structure behind published evidence. It shifts attention from an isolated reported result to the larger analytic context that produced it.

The goal is to make features such as analytic flexibility, multiplicity, preregistration, robustness, and interpretability more visible across the scientific literature—and ultimately to help readers understand not only what a study found, but the analytic context in which that result should be interpreted.

The Open Inference Project

Better inference requires making the analytic context of evidence visible.

Why bring this to the Research Forum?

The presentation was intentionally framed as work in progress. The empirical analysis, measurement strategy, and broader Open Inference framework were presented while they were still open to criticism and revision.

That reflects the purpose of the SoCO Research Forum itself: bringing ideas forward early enough that colleagues can challenge assumptions, expose weaknesses, and improve the work before the story becomes fixed.

Research Forum Presentation Archive

Who joined us?

The attendance record is preserved as part of the meeting recap. The roster below is limited to names and institutional affiliations.

Attendance roster View names and affiliations

This roster records meeting attendance and institutional affiliation only.

38 people represented Alphabetical · affiliations from the SoCO lookup table
Name Affiliation
Adewole S. Adamson The University of Texas at Austin
Adewunmi O. Adelaja Beth Israel Lahey
Ajay N. Sharma Mass General Brigham
Aleigha R. Lawless Mass General Brigham
Bailey Claire Smith Mass General Brigham
Ben Gratz University of Washington
Charlie Li Washington University in St. Louis
Dan Hippe University of Washington
David J Savage University of New Mexico Comprehensive Cancer Center
David M. Miller Mass General Brigham
Emily Godwin Summers University of North Carolina
Frances Collichio University of North Carolina
Haroutyun Joulfayan Washington University School of Medicine in St. Louis
Howard L. Kaufman Mass Eye and Ear
Isaac Brownell National Institutes of Health
Itai M. Pashtan Dana-Farber Cancer Institute
Jack Kollings University of Washington
Jessica L. Fewkes Mass Eye and Ear
Juliane Andrade Czapla Mass General Brigham
Justine V. Cohen Dana-Farber Cancer Institute
Kathryn Bollin Scripps Health
Krista Lachance University of Washington
Laura Ferris University of North Carolina
Lisa Zaba Stanford University
Mehran Behruj Yusuf University of Alabama at Birmingham
Michael Povelaitis University of North Carolina
Paul Nghiem University of Washington
Peter Ch’en University of Washington
Ray Cheever University of North Carolina
Rhoda Alani Mass General Brigham
Ross D. Merkin Mass General Brigham
Sameer Gupta Mass Eye and Ear
Shailender Bhatia University of Washington
Song Park University of Washington
Sonia Cohen Mass General Brigham
Todd Franklin Pearson Mass General Brigham
Vern Sondak Moffitt Cancer Center
Vishal Patel George Washington University

Society of Cutaneous Oncology Research Forum. Research discussions presented through the Forum may include work in progress and should be interpreted in that context.

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