Evidence map›Paper›PMID 37434630›Full record

ArticleJournal of applied statistics2023

Efficient estimation of a Cox model when integrating the subgroup incidence rate information.

Pei-Fang Su, Junjiang Zhong, Yi-Chia Liu, Tzu-Hsuan Lin, Huang-Tz Ou

Open access · greenAbstract read
In one paragraph

Article in Journal of applied statistics, 2023. 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
0.2field-weighted citation impact, top 45% of its field
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, 1 citations in OpenAlex.

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

5 authors at 4 institutions in 3 countries.

Pei-Fang SuDepartment of Statistics, National Cheng Kung University, Tainan, Taiwan.ORCID https://orcid.org/0000-0002-1149-8022
Junjiang ZhongSchool of Mathematics and Statistics, Xiamen University of Technology, Xiamen, People's Republic of China.
Yi-Chia LiuThe Center for Quantitative Sciences, Clinical Medicine Research Center, National Cheng Kung University Hospital, Tainan, Taiwan.
Tzu-Hsuan LinDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Huang-Tz OuInstitute of Clinical Pharmacy and Pharmaceutical Sciences, Department of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
National Cheng Kung University · TWNational Cheng Kung University Hospital · TWUniversity of Michigan · USXiamen University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Incidence rates for diseases are widely used in the field of medical research because they lead to clear and simple physical and clinical interpretations. In this study, we propose an efficient estimation method that incorporates auxiliary subgroup information related to the incidence rate into the estimation of the Cox proportional hazard model. The results show that utilizing the incidence rate information improves the efficiency of the estimation of regression parameters based on the double empirical likelihood method compared to that for conventional models that do not incorporation such information. We show that estimators of regression parameters asymptotically follow a multivariate normal distribution with a variance-covariance matrix that can be consistently estimated. Simulation results indicate that the proposed estimators significantly increase efficiency. Finally, an example of the effects of type 2 diabetes on stroke is applied to demonstrate the proposed method.

Indexed as

Double empirical likelihoodincidence raterelative efficiencytype 2 diabetes

Identifiers

PMID37434630
PMCPMC10332198
OpenAlexW4225420908

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.