Biometrics

About Biometrics

Biometrics: Journal Overview

Biometrics is a quarterly peer-reviewed scientific journal devoted to the application of statistics and mathematics to the biological sciences. It is published by Oxford University Press (OUP) on behalf of the International Biometric Society (IBS); OUP took over publication from 2024 (source: academic.oup.com/biometrics, official journal page, accessed October 2026). The journal's print ISSN is 0006-341X and its online ISSN is 1541-0420.

Biometrics occupies a distinctive position among statistics journals. Where general methodology journals evaluate papers primarily on theoretical novelty, Biometrics puts scientific application at the center: the journal emphasizes the role of statistics and mathematics in the biosciences, with the stated objective of promoting and extending the use of statistical and mathematical methods in the principal disciplines of the biosciences by reporting on the development and application of these methods. A centerpiece of most Biometrics articles is a scientific application that sets scientific or policy objectives, motivates methods development, and demonstrates how new methods operate in practice (source: official journal aims, academic.oup.com/biometrics and IBS materials).

The journal's reach extends across clinical trials, epidemiology, genetics, ecology, agriculture, and environmental science — any bioscience domain where quantitative reasoning must meet real data. Because its papers are anchored in genuine scientific problems, Biometrics has historically been the venue where landmark biostatistical methods first appeared in a form that working scientists could actually use, from inter-rater agreement measures to publication-bias diagnostics.

According to the official Oxford Academic journal page, Biometrics carries a 2025 Journal Impact Factor of 1.6, a 5-year Impact Factor of 2.4, and a cited half-life of 35.1 years — the unusually long half-life reflecting how durable the journal's methodological contributions are (source: academic.oup.com/biometrics, accessed October 2026). The journal is a member of the Committee on Publication Ethics (COPE) and subscribes to its principles.

History of Biometrics

Biometrics was first published in 1945 under the title Biometrics Bulletin, and adopted its shorter current title in 1947 (source: en.wikipedia.org/wiki/Biometrics_(journal), citing the journal's publication history). That same year, the International Biometric Society itself came into being: at a conference held at Woods Hole, Massachusetts, in September 1947, the Biometric Society was founded as an international organization devoted to the mathematical and statistical aspects of biology, with R. A. Fisher as its first president and C. I. Bliss as secretary (source: Nature 161:271, 1948, reporting the Society's formation). The Society's journal — first the Bulletin, then Biometrics — has been its flagship publication ever since; the JSTOR archive holds Biometrics from 1947–2020 and the Biometrics Bulletin from 1945–1946 (source: jstor.org/publisher/ibs).

Fisher's shadow looms large over the journal's early history: a memorial edition honoring R. A. Fisher was published in 1964, two years after his death (source: en.wikipedia.org/wiki/Biometrics_(journal)). In a survey of statistics researchers' opinions, Biometrics was ranked fifth overall among 40 statistics journals, and second only to the Journal of the American Statistical Association in the ranking provided by biometrics specialists — a testament to its standing among applied statisticians (source: en.wikipedia.org/wiki/Biometrics_(journal)).

For much of its history the journal was published by Wiley on behalf of the IBS. In a notable recent development, Oxford University Press became the publisher from 2024 (source: academic.oup.com/biometrics). Under OUP, the journal continues quarterly publication with the same applied-methodology identity, now supported by OUP's Read and Publish agreements that let authors at participating institutions publish open access using institutional funds.

The journal's scope has grown with the biosciences themselves. Early volumes were dominated by agricultural experiments, field trials, and the analysis of designed experiments — the biometric tradition of Fisher's Rothamsted. As biology changed, so did Biometrics: clinical trials methodology, survival analysis, longitudinal data analysis, missing-data methods, meta-analysis, statistical genetics, genomics, spatial epidemiology, and causal inference have all found a home in its pages. OpenAlex's topic classification of the journal's ~11,300 works reflects this breadth: the largest topic clusters are probability and statistical research, statistical methods and Bayesian inference, statistical methods in clinical trials, statistical methods and inference, and statistical methods and applications, with significant representation of causal inference, epidemiology, experimental design, genetics, and spatial statistics (source: api.openalex.org/sources/issn:0006-341X, October 2026).

That the journal's cited half-life stands at 35.1 years — far longer than most science journals — is itself a historical statement: Biometrics papers do not merely report findings, they supply tools. A method for measuring observer agreement or adjusting a meta-analysis for publication bias remains useful decades after publication, so the journal's back catalog stays alive in a way that few journals' do. The archive, continuous from 1945 through the present across the Bulletin and Biometrics titles, is effectively a museum of applied statistics in the twentieth and twenty-first centuries: one can trace the rise of clinical trials, the mathematization of epidemiology, and the genomic turn of biology through its tables of contents.

Editorial Leadership

As listed on the official Oxford Academic journal page (accessed October 2026), Biometrics is led by three Co-Editors — Cécile Proust-Lima, Erica Moodie, and Jessica Kasza — with Geert Molenberghs serving as Executive Editor (source: academic.oup.com/biometrics). Editorial boards at journals of this size rotate regularly, so readers should check the official journal page for the current masthead; earlier configurations of the editorship have included Alan Welsh, Debashish Ghosh, and Mark Brewer alongside Molenberghs (source: en.wikipedia.org/wiki/Biometrics_(journal)).

The co-editor model reflects the journal's breadth: with submissions spanning clinical trials, genomics, ecology, and environmental statistics, no single editor could cover the full disciplinary range. Below the editors sits an international editorial board of associate editors drawn from biostatistics departments, public-health schools, and statistics departments worldwide, matched to manuscripts by subject-matter expertise.

The journal also runs Best Paper and Reviewer Awards (recently for 2022 and 2023, selected by the co-editors), signaling an editorial culture that values both outstanding published work and the often-invisible labor of peer review (source: academic.oup.com/biometrics).

Aims & Scope

Biometrics' official aims are unusually explicit about what the journal is for. In paraphrase, the journal exists to promote and extend statistical and mathematical methods in the biosciences by publishing papers that develop and apply those methods. The defining editorial expectation is that most articles center on a scientific application — one that sets scientific or policy objectives, motivates the development of the method, and demonstrates the method in operation (source: official journal aims, academic.oup.com/biometrics).

In practical terms, this means a Biometrics submission is typically judged on three things: the novelty and correctness of the statistical or mathematical method, the genuineness of the scientific problem it addresses, and the clarity with which the paper connects the two. Purely theoretical papers without a demonstrated application, and purely applied papers without methodological development, are harder fits. Typical subject matter includes:

• Clinical trials and medical statistics: design and analysis of randomized trials, survival analysis, interim monitoring, adaptive designs.
• Longitudinal and correlated data: mixed-effects models, generalized estimating equations, joint models for longitudinal and time-to-event data.
• Epidemiology and public health: infectious-disease modeling, environmental epidemiology, spatial statistics, meta-analysis and evidence synthesis.
• Genetics and genomics: statistical genetics, high-dimensional genomic data, imaging genetics.
• Ecology, agriculture, and environmental science: capture–recapture, species-distribution modeling, designed experiments, environmental monitoring.
• Causal inference and missing data: methods for observational studies, missing-data and measurement-error problems.

The journal publishes research articles, and its online archive extends back to 1947, making it one of the deepest continuous records of applied statistical methodology in existence.

Key Facts and Metrics

The table below collects the journal's verifiable bibliographic facts and metrics, with sources and dates. Citation metrics are updated annually; readers should verify current figures on the official pages.

Landmark Papers Published in Biometrics

Over eight decades, Biometrics has published papers that reshaped entire subfields of statistics. The six below are among the journal's most-cited articles (citation counts from OpenAlex, October 2026) and each DOI was verified against the Crossref registry for this profile. They illustrate the journal's signature blend of method and application.

1. Landis, J. R. & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
DOI: 10.2307/2529310 (verified via Crossref)
This paper introduced the widely used interpretive scale for the kappa coefficient of inter-rater agreement (slight, fair, moderate, substantial, almost perfect) and gave the definitive treatment of measuring observer agreement for categorical data. With tens of thousands of citations — OpenAlex records over 81,000 citing works — it is one of the most-cited statistics papers ever published, and it remains the standard citation whenever researchers report agreement between raters, diagnostic tests, or classifiers in medicine and the social sciences.

2. Laird, N. M. & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
DOI: 10.2307/2529876 (verified via Crossref)
Laird and Ware's paper provided the now-canonical framework for analyzing longitudinal data with random-effects (linear mixed) models, including EM-algorithm-based estimation for unbalanced data. It effectively created the modern applied toolkit for repeated-measures analysis in clinical trials and epidemiology, and its influence runs through every mixed-model software implementation in use today.

3. DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44(3), 837–845.
DOI: 10.2307/2531595 (verified via Crossref)
The DeLong test for comparing correlated ROC curves — accounting for the fact that two diagnostic tests evaluated on the same patients produce correlated area-under-the-curve estimates — became the default method in medical diagnostics. Whenever a paper reports that one biomarker's AUC is "significantly higher" than another's, the statistical machinery almost always traces back to this Biometrics article.

4. Duncan, D. B. (1955). Multiple range and multiple F tests. Biometrics, 11(1), 1–42.
DOI: 10.2307/3001478 (verified via Crossref)
Duncan's multiple range test, developed for comparing treatment means in agricultural and biological experiments, became one of the most widely applied — and debated — multiple-comparison procedures in the experimental sciences. Published when the journal was still young, it exemplifies Biometrics' roots in designed experiments.

5. Begg, C. B. & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50(4), 1088–1101.
DOI: 10.2307/2533446 (verified via Crossref)
Begg and Mazumdar formalized the rank-correlation test for publication bias in meta-analysis — the idea that a funnel plot's asymmetry can be tested by correlating standardized effect sizes with their variances. Published as systematic reviewing was becoming central to evidence-based medicine, it gave meta-analysts a principled way to ask whether the literature they were synthesizing was itself biased.

6. Duval, S. & Tweedie, R. (2000). Trim and fill: A simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455–463.
DOI: 10.1111/j.0006-341X.2000.00455.x (verified via Crossref)
The "trim and fill" method — a rank-based data-augmentation technique that estimates the number of missing studies and adjusts the pooled effect — is now implemented in every major meta-analysis software package. It is the rare statistical method whose name has become a verb: researchers routinely speak of "trimming and filling" a funnel plot.

Publishing in Biometrics

Manuscripts are submitted through Oxford Academic's online submission system (linked from the official journal page at academic.oup.com/biometrics); the page also provides author resources covering the submission process, article promotion, and publishing ethics. Because the journal's identity rests on the application–method connection, authors are well advised to lead with the scientific problem: the abstract and introduction should make clear what biological or medical question is at stake before the method is developed.

Practical points for prospective authors, drawn from the official journal page:

• Read and Publish deals: Authors at institutions participating in OUP's Read and Publish agreements may be able to publish their paper open access using funds available through their institution's agreement — worth checking before choosing the subscription or OA route.
• Email alerts: Readers can register for table-of-contents email alerts as soon as new issues are published online.
• Library recommendation: The journal provides a form to recommend Biometrics to a library, useful for readers whose institutions lack access.
• High-impact collections: OUP curates collections of the journal's most-read, most-cited, and most-discussed articles — a useful entry point for new readers wanting to see what the field values.

The journal is not fully open access (it operates a hybrid model), and neither OUP nor the journal officially publishes an acceptance rate. As with any leading applied journal, authors should expect rigorous review focused as much on the adequacy of the application as on the novelty of the method.

Frequently Asked Questions

What is the ISSN of Biometrics?
Print ISSN 0006-341X; online ISSN 1541-0420.

Who publishes Biometrics?
Oxford University Press, on behalf of the International Biometric Society (from 2024; previously Wiley).

When was Biometrics founded?
1945, as the Biometrics Bulletin; it took the title Biometrics in 1947, the same year the International Biometric Society was founded at Woods Hole, Massachusetts.

Who edits Biometrics?
As listed on the official Oxford Academic page (October 2026): Co-Editors Cécile Proust-Lima, Erica Moodie, and Jessica Kasza, with Geert Molenberghs as Executive Editor. Editorial boards rotate, so check the official page for the current masthead.

What is the impact factor of Biometrics?
The official journal page reports a 2025 Journal Impact Factor of 1.6 and a 5-year Impact Factor of 2.4, with a cited half-life of 35.1 years — reflecting the long useful life of its methodological papers.

Is Biometrics open access?
No — it is a hybrid journal. Authors can publish open access (including via OUP Read and Publish institutional agreements), but the journal is not fully open access. No APC figure is officially published on the journal page; OpenAlex reports a USD 3,780 list price sourced from DOAJ. Confirm current charges with the publisher.

What kinds of papers does Biometrics publish?
Peer-reviewed articles developing and applying statistical and mathematical methods in the biosciences — clinical trials, epidemiology, genetics, ecology, agriculture, and related fields — with a scientific application typically at the center of each paper.

Where can I read older issues of Biometrics?
The JSTOR archive holds Biometrics from 1947 to 2020 and the Biometrics Bulletin from 1945 to 1946; recent content is on Oxford Academic.