Evidence map›Paper›PMID 26091237›Full record

ArticlePloS one2015

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models.

Weng Kee Wong, Ray-Bing Chen, Chien-Chih Huang, Weichung Wang

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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.

Weng Kee WongDepartment of Biostatistics, University of California, Los Angeles, USA.
Ray-Bing ChenDepartment of Statistics, National Cheng Kung University, Taiwan.
Chien-Chih HuangDepartment of Mathematics, National Taiwan University, Taipei, Taiwan.
Weichung WangInstitute of Applied Mathematical Sciences, National Taiwan University, Taiwan.

Funding

Designing Efficient Designs under Model Uncertainty for Biological StudiesR01GM107639 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WONG, WENG K · 2014 to 2017
$1.0M
NIGMS NIH HHS R01 GM107639NIGMS NIH HHS R01GM107639
6 · The paper itself

Abstract

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1].

Indexed as

Models, TheoreticalAlgorithms

Identifiers

PMID26091237
PMCPMC4474858

What OpenQuestion holds

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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.