Evidence map›Paper›PMID 33768490›Full record

ArticleLifetime data analysis2021

The semiparametric accelerated trend-renewal process for recurrent event data.

Chien-Lin Su, Russell J Steele, Ian Shrier

Abstract read
PubMed Publisher
In one paragraph

Article in Lifetime data analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Chien-Lin SuDepartment of Mathematics and Statistics, McGill University, Montréal, QC, Canada. chien-lin.su@mail.mcgill.ca.ORCID 0000-0002-0664-8583
Russell J SteeleDepartment of Mathematics and Statistics, McGill University, Montréal, QC, Canada.
Ian ShrierCentre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada.

Funding

CIHR CHRPJ/478521-2015
6 · The paper itself

Abstract

Recurrent event data arise in many biomedical longitudinal studies when health-related events can occur repeatedly for each subject during the follow-up time. In this article, we examine the gap times between recurrent events. We propose a new semiparametric accelerated gap time model based on the trend-renewal process which contains trend and renewal components that allow for the intensity function to vary between successive events. We use the Buckley-James imputation approach to deal with censored transformed gap times. The proposed estimators are shown to be consistent and asymptotically normal. Model diagnostic plots of residuals and a method for predicting number of recurrent events given specified covariates and follow-up time are also presented. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to two real data sets.

Indexed as

Longitudinal StudiesComputer SimulationHumansAccelerated transformed gap time modelBuckley–James imputationModel diagnostic plotsPredictionRecurrent eventsTrend-renewal process

Identifiers

What OpenQuestion holds

Textmetadata
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.