Evidence map›Paper›PMID 30895308›Full record

ReviewBriefings in bioinformatics2020

Sensitivity and specificity of information criteria.

John J Dziak, Donna L Coffman, Stephanie T Lanza, Runze Li, Lars S Jermiin

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 225 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
225citing papers in PubMed, 2 pooled it
–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

225 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  11. Multidimensional workplace stressors differentially impact cognitive trajectories.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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  15. Factors associated with health state valuations: a secondary analysis of an EQ-5D-3L valuation study from Jordan.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
    Article
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  20. Observational

165 more citing papers are in PubMed but not listed here.

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.

John J DziakMethodology Center at Penn State.
Donna L CoffmanDepartment of Epidemiology and Biostatistics at Temple University.
Stephanie T LanzaDepartment of Biobehavioral Health and a principal investigator at the Methodology Center.
Runze LiDepartment of Statistics and a principal investigator in the Methodology Center at Penn State.
Lars S JermiinResearch School of Biology at the Australian National University and a visiting researcher at the Earth Institute and School of Biology and Environmental Science, University College Dublin.

Funding

Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, RUNZE · 2015 to 2019
$13.9M
NIDA NIH HHS P50 DA039838
6 · The paper itself

Abstract

Information criteria (ICs) based on penalized likelihood, such as Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.

Indexed as

Models, TheoreticalBayes TheoremComputational BiologyLikelihood FunctionsSample SizeAkaike information criterionBayesian information criterionlatent class analysislikelihood ratio testingmodel selection

Identifiers

PMID30895308
PMCPMC7299313

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.