Evidence map›Paper›PMID 40947399›Full record

ArticleStatistics in medicine2025

Tutorial on Bayesian Functional Regression Using Stan.

Ziren Jiang, Ciprian Crainiceanu, Erjia Cui

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Ziren JiangDivision of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0002-5830-327X
Ciprian CrainiceanuDepartment of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.
Erjia CuiDivision of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA.

Funding

Statistical Methods for Multilevel Multivariate Functional StudiesR01NS060910 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Ciprian M Crainiceanu · 2009 to 2026
$8.3M
Novel application of Digital signals of movement, sleep and heart rhythms for detection of Alzheimer's Disease and Related DementiasR01AG075883 · NIA · JOHNS HOPKINS UNIVERSITY · PI Lin Yee Chen, Ciprian M Crainiceanu · 2022 to 2026
$3.8M
NIA NIH HHS R01 AG075883NIH HHS R01 AG075883NIH HHS R01 NS060910NINDS NIH HHS R01 NS060910
6 · The paper itself

Abstract

This manuscript provides step-by-step instructions for implementing Bayesian functional regression models using Stan. Extensive simulations indicate that the inferential performance of the methods is comparable to that of state-of-the-art frequentist approaches. However, Bayesian approaches allow for more flexible modeling and provide an alternative when frequentist methods are not available or may require additional development. Methods and software are illustrated using the accelerometry data from the National Health and Nutrition Examination Survey (NHANES).

Indexed as

SoftwareAccelerometryBayes TheoremComputer SimulationHumansModels, StatisticalNutrition SurveysRegression AnalysisBayesian data analysisfunctional Cox regressionfunctional data analysisfunctional principal component analysisstan

Identifiers

PMID40947399
PMCPMC12433833

What OpenQuestion holds

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LicenceCC BY
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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.