Evidence map›Paper›PMID 26108655›Full record

ArticleEuropean journal of epidemiology2015

The researcher and the consultant: from testing to probability statements.

Ghassan B Hamra, Andreas Stang, Charles Poole

Abstract read
PubMed Publisher
In one paragraph

Article in European journal of epidemiology, 2015. 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

3 authors.

Ghassan B HamraDepartment of Environmental and Occupational Health, Drexel University School of Public Health, 3215 Market St., Philadelphia, PA, 19104, USA. gbh28@drexel.edu.
Andreas StangCenter of Clinical Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Essen, Germany.
Charles PooleDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the first instalment of this series, Stang and Poole provided an overview of Fisher significance testing (ST), Neyman-Pearson null hypothesis testing (NHT), and their unfortunate and unintended offspring, null hypothesis significance testing. In addition to elucidating the distinction between the first two and the evolution of the third, the authors alluded to alternative models of statistical inference; namely, Bayesian statistics. Bayesian inference has experienced a revival in recent decades, with many researchers advocating for its use as both a complement and an alternative to NHT and ST. This article will continue in the direction of the first instalment, providing practicing researchers with an introduction to Bayesian inference. Our work will draw on the examples and discussion of the previous dialogue.

Indexed as

ConsultantsModels, StatisticalProbabilityResearch PersonnelBayes TheoremData Interpretation, StatisticalHumansResearch DesignBayesian inferenceConfidence intervalsProbability statementsSignificance testing

Identifiers

PMID26108655

What OpenQuestion holds

Textmetadata
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.