Evidence map›Paper›PMID 38223302›Full record

ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2023

Online multiple hypothesis testing.

David S Robertson, James M S Wason, Aaditya Ramdas

Abstract read
In one paragraph

Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. ADDIS-Graphs for Online Error Control With Application to Platform Trials.Biometrical journal. Biometrische Zeitschrift · 2025
    Article
  5. Article
  6. Federated epidemic surveillance.PLoS computational biology · 2025
    Article
  7. 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

3 authors.

David S RobertsonMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
James M S WasonPopulation Health Sciences Institute, Newcastle University, Newcastle, UK.
Aaditya RamdasDepartments of Statistics and Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

Funding

Department of Health IS-BRC-1215-20014Department of Health NIHR301614Medical Research Council MC_UP_1302/4Medical Research Council MC_UU_00002/14Medical Research Council MC_UU_00002/6Medical Research Council MR/N028171/1
6 · The paper itself

Abstract

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all

Indexed as

A/B testingdata repositoriesplatform trialstype I error rate

Identifiers

PMID38223302
PMCPMC7615519

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.