Evidence map›Paper›PMID 25143663›Full record

ArticleJournal of applied statistics2014

Longitudinal Data Analysis Using Bayesian-frequentist Hybrid Random Effects Model.

Le Chen, Ao Yuan, Aiyi Liu, Guanjie Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied statistics, 2014. 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

4 authors.

Le ChenBiostatistics and Bioinformatics Branch(BBB), Division of Intramural Population Health Research(DIPHR), Eunice Kennedy Shriver National Institutes of Child Health and Human Development(NICHD), NIH, Bethesda, Maryland, USA.
Ao YuanDepartment of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC, USA.
Aiyi LiuBiostatistics and Bioinformatics Branch(BBB), Division of Intramural Population Health Research(DIPHR), Eunice Kennedy Shriver National Institutes of Child Health and Human Development(NICHD), NIH, Bethesda, Maryland, USA.
Guanjie ChenCenter for Research on Genomics and Global Health, NHGRI, NIH, Bethesda, Maryland, USA.

Funding

Statistical Methods for Evaluation of BiomarkersZIAHD008875 · NICHD · EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT · PI LIU, AIYI · 2009 to 2025
$3.8M
Intramural NIH HHS Z99 HG999999
6 · The paper itself

Abstract

The mixed random effect model is commonly used in longitudinal data analysis within either frequentist or Bayesian framework. Here we consider a case, we have prior knowledge on partial-parameters, while no such information on rest. Thus, we use the hybrid approach on the random-effects model with partial-parameters. The parameters are estimated via Bayesian procedure, and the rest of parameters by the frequentist maximum likelihood estimation (MLE), simultaneously on the same model. In practices, we often know partial prior information such as, covariates of age, gender, and etc. These information can be used, and get accurate estimations in mixed random-effects model. A series of simulation studies were performed to compare the results with the commonly used random-effects model with and without partial prior information. The results in hybrid estimation (HYB) and Maximum likelihood estimation (MLE) were very close each other. The estimated

Indexed as

HybridLongitudinal dataSimulation

Identifiers

PMID25143663
PMCPMC4133133

What OpenQuestion holds

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Registered trials

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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.