Evidence map›Paper›PMID 34897773›Full record

ArticleStatistics in medicine2022

Determination of the number of observers needed to evaluate a subjective test and its application in two PD-L1 studies.

Gang Han, Michael J Schell, Emily S Reisenbichler, Bohong Guo, David L Rimm

Abstract read
In one paragraph

Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Multi-institutional Assessment of Pathologist Scoring HER2 Immunohistochemistry.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2023
    Article
  11. Development of an immunohistochemical assay for Siglec-15.Laboratory investigation; a journal of technical methods and pathology · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Gang HanDepartment of Epidemiology and Biostatistics, Texas A&M University School of Public Health, College Station, Texas, USA.ORCID 0000-0002-5781-5169
Michael J SchellDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, Florida, USA.
Emily S ReisenbichlerDepartment of Pathology, Saint Louis University School of Medicine, St. Louis, Missouri, USA.
Bohong GuoDepartment of Epidemiology and Biostatistics, Texas A&M University School of Public Health, College Station, Texas, USA.
David L RimmDepartment of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA.

Funding

Yale Pathology Tissue Services Shared ResourceP30CA016359 · NCI · YALE UNIVERSITY · PI DAVID L RIMM · 1985 to 2026
$85.0M
Yale SPORE in Lung Cancer (YSILC): The Biology and Personalized Treatment of Lung CancerP50CA196530 · NCI · YALE UNIVERSITY · PI Shuangge Ma · 2015 to 2026
$31.1M
NCI NIH HHS P30 CA016359NCI NIH HHS P50 CA196530
6 · The paper itself

Abstract

In pathological studies, subjective assays, especially companion diagnostic tests, can dramatically affect treatment of cancer. Binary diagnostic test results (ie, positive vs negative) may vary between pathologists or observers who read the tumor slides. Some tests have clearly defined criteria resulting in highly concordant outcomes, even with minimal training. Other tests are more challenging. Observers may achieve poor concordance even with training. While there are many statistically rigorous methods for measuring concordance between observers, we are unaware of a method that can identify how many observers are needed to determine whether a test can reach an acceptable concordance, if at all. Here we introduce a statistical approach to the assessment of test performance when the test is read by multiple observers, as would occur in the real world. By plotting the number of observers against the estimated overall agreement proportion, we can obtain a curve that plateaus to the average observer concordance. Diagnostic tests that are well-defined and easily judged show high concordance and plateau with few interobserver comparisons. More challenging tests do not plateau until many interobserver comparisons are made, and typically reach a lower plateau or even 0. We further propose a statistical test of whether the overall agreement proportion will drop to 0 with a large number of pathologists. The proposed analytical framework can be used to evaluate the difficulty in the interpretation of pathological test criteria and platforms, and to determine how pathology-based subjective tests will perform in the real world. The method could also be used outside of pathology, where concordance of a diagnosis or decision point relies on the subjective application of multiple criteria. We apply this method in two recent PD-L1 studies to test whether the curve of overall agreement proportion will converge to 0 and determine the minimal sufficient number of observers required to estimate the concordance plateau of their reads.

Indexed as

B7-H1 AntigenCorrelation of DataHumansImmunohistochemistryObserver VariationReproducibility of ResultsB7-H1 AntigenCD274 protein, humanBinomial distributionconcordanceinflated binomial distributionoverall agreement proportionpathological tests

Identifiers

PMID34897773
PMCPMC10243718

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.