Evidence map›Paper›PMID 19104505›Full record

ArticleThe pharmacogenomics journal2009

A new statistical screening approach for finding pharmacokinetics-related genes in genome-wide studies.

Y Sato, N M Laird, K Nagashima, R Kato, T Hamano, A Yafune, N Kaniwa, Y Saito, E Sugiyama, S-R Kim and 7 more

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in The pharmacogenomics journal, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

17 authors.

Y SatoDepartment of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
N M Laird
K Nagashima
R Kato
T Hamano
A Yafune
N Kaniwa
Y Saito
E Sugiyama
S-R Kim
J Furuse
H Ishii
H Ueno
T Okusaka
N Saijo
J-I Sawada
T Yoshida

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomedical researchers usually test the null hypothesis that there is no difference of the population mean of pharmacokinetics (PK) parameters between genotypes by the Kruskal-Wallis test. Although a monotone increasing pattern with a number of alleles is expected for PK-related genes, the Kruskal-Wallis test does not consider a monotonic response pattern. For detecting such patterns in clinical and toxicological trials, a maximum contrast method has been proposed. We show how that method can be used with pharmacogenomics data to a develop test of association. Further, using simulation studies, we compare the power of the modified maximum contrast method to those of the maximum contrast method and the Kruskal-Wallis test. On the basis of the results of those studies, we suggest rules of thumb for which statistics to use in a given situation. An application of all three methods to an actual genome-wide pharmacogenomics study illustrates the practical relevance of our discussion.

Indexed as

Models, GeneticModels, StatisticalPharmacokineticsPolymorphism, Single NucleotideComputer SimulationGenome-Wide Association StudyGenotypeHumansMonte Carlo MethodPharmacogeneticsPhenotype

Identifiers

PMID19104505

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.