Evidence map›Paper›PMID 39014288›Full record

ArticlePsychometrika2024

Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM.

Sun-Joo Cho, Sarah Brown-Schmidt, Sharice Clough, Melissa C Duff

Abstract read
In one paragraph

Article in Psychometrika, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

Sun-Joo ChoVanderbilt University, Nashville, USA. sj.cho@vanderbilt.edu.
Sarah Brown-SchmidtVanderbilt University, Nashville, USA.
Sharice CloughVanderbilt University Medical Center, Nashville, USA.
Melissa C DuffVanderbilt University Medical Center, Nashville, USA.

Funding

Language processing in context in individuals with Alzheimer’s diseaseR01DC017926 · NIDCD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Sarah Brown-Schmidt, Melissa C Duff · 2020 to 2026
$5.2M
NIDCD NIH HHS R01 DC017926NINR NIH HHS NIDCD grant R01 NIH DC017926
6 · The paper itself

Abstract

This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf , 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.

Indexed as

by-variable smooth functioneye-tracking datageneralized additive mixed modelgroup comparisonsintensive binary longitudinal data

Identifiers

PMID39014288
PMCPMC12483702

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.