Evidence map›Paper›PMID 37463841›Full record

ArticleBiometrics2023

Discussion of "Optimal test procedures for multiple hypotheses controlling the familywise expected loss" by Willi Maurer, Frank Bretz, and Xiaolei Xun.

Sudipto Banerjee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Sudipto BanerjeeDepartment of Biostatistics, University of California Los Angeles, Los Angeles, California, USA.ORCID 0000-0002-2239-208X

Funding

Hierarchical Modeling and Analysis for Large Spatially and Temporally Misaligned Data in Environmental Health ApplicationsR01ES027027 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BANERJEE, SUDIPTO · 2017 to 2021
$1.7M
Flexible Bayesian Hierarchical Models for Estimating Inhalation ExposuresR01ES030210 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BANERJEE, SUDIPTO · 2019 to 2022
$1.6M
Bayesian Modeling and Inference for High-Dimensional Disease Mapping and Boundary Detection"R01GM148761 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Sudipto Banerjee · 2023 to 2026
$1.2M
NIEHS NIH HHS R01 ES027027NIEHS NIH HHS R01ES027027NIEHS NIH HHS R01 ES030210NIEHS NIH HHS R01ES030210NIGMS NIH HHS R01 GM148761
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Research DesignData Interpretation, Statistical

Identifiers

PMID37463841
PMCPMC10794545

What OpenQuestion holds

Textmetadata
LicenceTDM
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.