Evidence map›Paper›PMID 32952273›Full record

ArticleCommunications in statistics: theory and methods2019

Parameter Estimation for Semiparametric Ordinary Differential Equation Models.

Hongqi Xue, Arun Kumar, Hulin Wu

Abstract read
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In one paragraph

Article in Communications in statistics: theory and methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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.

Hongqi XueiCardiac Technologies, 150 Allens Creek Rd, Rochester, NY 14618.
Arun KumarLivanova, 100 Cyberonics Blvd, Houston, TX 77058.
Hulin WuThe university of texas health science center of Houston, 7000 Fannin St 1200, Houston, TX 77030.

Funding

UNIVERSITY OF ROCHESTER'S RESEARCH ASSOCIATE ORGANIZATIONUL1RR024160 · NCRR · UNIVERSITY OF ROCHESTER · PI PEARSON, THOMAS ARTHUR · 2006 to 2011
$38.7M
Statistical Methods for ODE Models in AIDS ResearchR01AI087135 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI WU, HULIN · 2010 to 2019
$3.2M
NCRR NIH HHS UL1 RR024160NIAID NIH HHS HHSN272201000055CNIAID NIH HHS R01 AI087135
6 · The paper itself

Abstract

We propose a new class of two-stage parameter estimation methods for semiparametric ordinary differential equation (ODE) models. In the first stage, state variables are estimated using a penalized spline approach; In the second stage, form of numerical discretization algorithms for an ODE solver is used to formulate estimating equations. Estimated state variables from the first stage are used to obtain more data points for the second stage. Asymptotic properties for the proposed estimators are established. Simulation studies show that the method performs well, especially for small sample. Real life use of the method is illustrated using Influenza specific cell-trafficking study.

Indexed as

Data augmentation estimationOrdinary differential equationPenalized splineSemiparametric coefficient modelsTwo-stage estimation

Identifiers

PMID32952273
PMCPMC7500512

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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.