Evidence map›Paper›PMID 40290451›Full record

ArticleStatistica Sinica2023

NONPARAMETRIC ESTIMATION AND TESTING FOR PANEL COUNT DATA WITH INFORMATIVE TERMINAL EVENT.

Xiangbin Hu, Li Liu, Ying Zhang, Xingqiu Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistica Sinica, 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
–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

4 authors.

Xiangbin HuThe Hong Kong Polytechnic University, Wuhan University and University of Nebraska Medical Center.
Li LiuThe Hong Kong Polytechnic University, Wuhan University and University of Nebraska Medical Center.
Ying ZhangThe Hong Kong Polytechnic University, Wuhan University and University of Nebraska Medical Center.
Xingqiu ZhaoThe Hong Kong Polytechnic University, Wuhan University and University of Nebraska Medical Center.

Funding

Tracking and Evaluation CoreU54GM115458 · NIGMS · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI RIZZO, MATTHEW · 2016 to 2025
$42.8M
NIGMS NIH HHS U54 GM115458
6 · The paper itself

Abstract

Informative terminal events often occur in the long term recurrent event follow-up studies. To reflect their effects on recurrent event processes explicitly, we propose a reversed nonparametric mean model for panel count data with a terminal event subject to right censoring. This model enjoys meaningful interpretation for applications and robustness for statistical inference. Treating the distribution of the right-censored terminal event time as a nuisance functional parameter, we develop a two-stage estimation procedure by combining the Kaplan-Meier estimator and nonparametric sieve estimation techniques. The consistency, convergence rate and asymptotic normality of the proposed nonparametric estimator are established. Then we construct a class of new statistics for two-sample test. The asymptotic properties of the new tests are established and evaluated by extensive simulation studies. Panel count data from Chinese Longitudinal Healthy Longevity study are analyzed using the proposed method for illustration.

Indexed as

Monotone polynomial splineNonparametric testPanel count dataTerminal eventTwo-stage estimation

Identifiers

PMID40290451
PMCPMC12030113

What OpenQuestion holds

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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.