Evidence map›Paper›PMID 35712522›Full record

ArticleJapanese journal of statistics and data science2021

Analysis of cyclic recurrent event data with multiple event types.

Chien-Lin Su, Feng-Chang Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Japanese journal of statistics and data science, 2021. 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. Article
  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

2 authors.

Chien-Lin SuDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.ORCID 0000-0002-0664-8583
Feng-Chang LinDepartment of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.ORCID 0000-0002-2638-1775

Funding

Re-Entry Supplement: Investigation of Oral Microbial Enzymes for the Detection and Treatment of Periodontal DiseaseUL1TR002489 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, SHAHEEN, NICHOLAS J · 2018 to 2022
$48.6M
UNC Clinical and Translational Science AwardUL1TR000083 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI RUNGE, MARSCHALL S. · 2012 to 2013
$11.6M
NCATS NIH HHS UL1 TR000083NCATS NIH HHS UL1 TR002489
6 · The paper itself

Abstract

Recurrent event data frequently arise in practice, and in some cases, the event process has cyclic or periodic components. We propose a semiparametric rate model with multiple event types that have such features. Generalized estimating equations are used for the estimation of regression coefficients after profiling the baseline rate function with a fully nonparametric estimator. The proposed estimators are shown to be consistent and asymptotically Gaussian. Their finite-sample behavior is assessed through simulation experiments. The predictability of the model with and without the cyclic component is also compared. With the cyclic component, our model improves the predictability of a conventional model without the cyclic feature. Data on recurrent fire alarms in Blenheim, New Zealand, are used for illustration purposes.

Indexed as

Cyclic baseline rate functionFire serviceGeneralized estimating equationsMarginal rate modelPrediction

Identifiers

PMID35712522
PMCPMC9197686

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.