Evidence map›Paper›PMID 31929720›Full record

ArticleJournal of the Korean Statistical Society2019

A Comparison of Monte Carlo Methods for Computing Marginal Likelihoods of Item Response Theory Models.

Yang Liu, Guanyu Hu, Lei Cao, Xiaojing Wang, Ming-Hui Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Korean Statistical Society, 2019. 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

5 · Who and what money

Authors and funding

5 authors.

Yang LiuDepartment of Statistics, University of Connecticut, Storrs, Connecticut, U.S.A.
Guanyu HuDepartment of Statistics, University of Connecticut, Storrs, Connecticut, U.S.A.
Lei CaoDepartment of Statistics, University of Connecticut, Storrs, Connecticut, U.S.A.
Xiaojing WangDepartment of Statistics, University of Connecticut, Storrs, Connecticut, U.S.A.
Ming-Hui ChenDepartment of Statistics, University of Connecticut, Storrs, Connecticut, U.S.A.

Funding

Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVIDIAN, MARIE, KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
Bayesian Approaches to Model Selection for Survival DataR01GM070335 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI IBRAHIM, JOSEPH G · 2003 to 2019
$4.0M
INFERENCE IN REGRESSION MODELS WITH MISSING COVARIATESR01CA074015 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI IBRAHIM, JOSEPH G · 1997 to 2010
$1.7M
NCI NIH HHS P01 CA142538NCI NIH HHS R01 CA074015NIGMS NIH HHS R01 GM070335
6 · The paper itself

Abstract

Nowadays, Bayesian methods are routinely used for estimating parameters of item response theory (IRT) models. However, the marginal likelihoods are still rarely used for comparing IRT models due to their complexity and a relatively high dimension of the model parameters. In this paper, we review Monte Carlo (MC) methods developed in the literature in recent years and provide a detailed development of how these methods are applied to the IRT models. In particular, we focus on the "best possible" implementation of these MC methods for the IRT models. These MC methods are used to compute the marginal likelihoods under the one-parameter IRT model with the logistic link (1PL model) and the two-parameter logistic IRT model (2PL model) for a real English Examination dataset. We further use the widely applicable information criterion (WAIC) and deviance information criterion (DIC) to compare the 1PL model and the 2PL model. The 2PL model is favored by all of these three Bayesian model comparison criteria for the English Examination data.

Indexed as

Bayes factorCMDEIWMDEMarginal posterior densityMCMC

Identifiers

PMID31929720
PMCPMC6953617

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.