Evidence map›Paper›PMID 35317852›Full record

ArticleBMC research notes2022

Statistical inference for ordinal predictors in generalized additive models with application to Bronchopulmonary Dysplasia.

Jan Gertheiss, Fabian Scheipl, Tina Lauer, Harald Ehrhardt

Abstract read
In one paragraph

Article in BMC research notes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Ordinal state-trait regression for intensive longitudinal data.The British journal of mathematical and statistical psychology · 2023
    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.

Jan GertheissSchool of Economics and Social Sciences, Helmut Schmidt University/University of the Federal Armed Forces, Hamburg, Germany. jan.gertheiss@hsu-hh.de.ORCID http://orcid.org/0000-0001-6777-4746
Fabian ScheiplDepartment of Statistics, Ludwig Maximilians University, Munich, Germany.
Tina LauerDepartment of General Pediatrics and Neonatology, Justus Liebig University, Giessen, Germany.
Harald EhrhardtDepartment of General Pediatrics and Neonatology, Justus Liebig University, Giessen, Germany.

Funding

Deutsche Forschungsgemeinschaft GE2353/2-1
6 · The paper itself

Abstract

objectiveDiscrete but ordered covariates are quite common in applied statistics, and some regularized fitting procedures have been proposed for proper handling of ordinal predictors in statistical models. Motivated by a study from neonatal medicine on Bronchopulmonary Dysplasia (BPD), we show how quadratic penalties on adjacent dummy coefficients of ordinal factors proposed in the literature can be incorporated in the framework of generalized additive models, making tools for statistical inference developed there available for ordinal predictors as well.

resultsThe approach presented allows to exploit the scale level of ordinally scaled factors in a sound statistical framework. Furthermore, several ordinal factors can be considered jointly without the need to collapse levels even if the number of observations per level is small. By doing so, results obtained earlier on the BPD data analyzed could be confirmed.

Indexed as

Bronchopulmonary DysplasiaHumansInfant, NewbornModels, StatisticalChronic lung diseaseLogit modelOrdinal dataRegularizationSmoothing penalty

Identifiers

PMID35317852
PMCPMC8939193

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.